Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach
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DmitryRyuminย
authored a
paper about 9 hours ago
DmitryRyuminย
authored a
paper about 10 hours ago
DmitryRyuminย
posted an update 9 months ago
Post
1381
๐๐๏ธ๐ New Research Alert - ICCV 2025 (Poster)! ๐๐๏ธ๐
๐ Title: Is Less More? Exploring Token Condensation as Training-Free Test-Time Adaptation ๐
๐ Description: Token Condensation as Adaptation (TCA) improves the performance and efficiency of Vision Language Models in zero-shot inference by introducing domain anchor tokens.
๐ฅ Authors: Zixin Wang, Dong Gong, Sen Wang, Zi Huang, Yadan Luo
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation (2410.14729)
๐ Repository: https://github.com/Jo-wang/TCA
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Session 1: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/session-1.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #TestTimeAdaptation #TokenCondensation #VisionLanguageModels #TrainingFreeAdaptation #ZeroShotLearning #EfficientAI #AI #ICCV2025 #ResearchHighlight
๐ Title: Is Less More? Exploring Token Condensation as Training-Free Test-Time Adaptation ๐
๐ Description: Token Condensation as Adaptation (TCA) improves the performance and efficiency of Vision Language Models in zero-shot inference by introducing domain anchor tokens.
๐ฅ Authors: Zixin Wang, Dong Gong, Sen Wang, Zi Huang, Yadan Luo
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation (2410.14729)
๐ Repository: https://github.com/Jo-wang/TCA
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Session 1: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/session-1.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #TestTimeAdaptation #TokenCondensation #VisionLanguageModels #TrainingFreeAdaptation #ZeroShotLearning #EfficientAI #AI #ICCV2025 #ResearchHighlight
DmitryRyuminย
posted an update 9 months ago
Post
2516
๐๐๏ธ๐ New Research Alert - ICCV 2025 (Oral)! ๐๐๏ธ๐
๐ Title: Diving into the Fusion of Monocular Priors for Generalized Stereo Matching ๐
๐ Description: The proposed method enhances stereo matching by efficiently combining unbiased monocular priors from vision foundation models. This method addresses misalignment and local optima issues using a binary local ordering map and pixel-wise linear regression.
๐ฅ Authors: Chengtang Yao, Lidong Yu, Zhidan Liu, Jiaxi Zeng, Yuwei Wu, and Yunde Jia
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Diving into the Fusion of Monocular Priors for Generalized Stereo Matching (2505.14414)
๐ Repository: https://github.com/YaoChengTang/Diving-into-the-Fusion-of-Monocular-Priors-for-Generalized-Stereo-Matching
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the 3D Pose Understanding Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/3d-pose-understanding.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #StereoMatching #MonocularDepth #VisionFoundationModels #3DReconstruction #Generalization #AI #ICCV2025 #ResearchHighlight
๐ Title: Diving into the Fusion of Monocular Priors for Generalized Stereo Matching ๐
๐ Description: The proposed method enhances stereo matching by efficiently combining unbiased monocular priors from vision foundation models. This method addresses misalignment and local optima issues using a binary local ordering map and pixel-wise linear regression.
๐ฅ Authors: Chengtang Yao, Lidong Yu, Zhidan Liu, Jiaxi Zeng, Yuwei Wu, and Yunde Jia
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Diving into the Fusion of Monocular Priors for Generalized Stereo Matching (2505.14414)
๐ Repository: https://github.com/YaoChengTang/Diving-into-the-Fusion-of-Monocular-Priors-for-Generalized-Stereo-Matching
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the 3D Pose Understanding Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/3d-pose-understanding.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #StereoMatching #MonocularDepth #VisionFoundationModels #3DReconstruction #Generalization #AI #ICCV2025 #ResearchHighlight
DmitryRyuminย
posted an update 9 months ago
Post
2848
๐๐๐ New Research Alert - ICCV 2025 (Oral)! ๐๐ค๐
๐ Title: Understanding Co-speech Gestures in-the-wild ๐
๐ Description: JEGAL is a tri-modal model that learns from gestures, speech and text simultaneously, enabling devices to interpret co-speech gestures in the wild.
๐ฅ Authors: @sindhuhegde , K R Prajwal, Taein Kwon, and Andrew Zisserman
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Understanding Co-speech Gestures in-the-wild (2503.22668)
๐ Web Page: https://www.robots.ox.ac.uk/~vgg/research/jegal
๐ Repository: https://github.com/Sindhu-Hegde/jegal
๐บ Video: https://www.youtube.com/watch?v=TYFOLKfM-rM
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Human Modeling Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/human-modeling.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #CoSpeechGestures #GestureUnderstanding #TriModalRepresentation #MultimodalLearning #AI #ICCV2025 #ResearchHighlight
๐ Title: Understanding Co-speech Gestures in-the-wild ๐
๐ Description: JEGAL is a tri-modal model that learns from gestures, speech and text simultaneously, enabling devices to interpret co-speech gestures in the wild.
๐ฅ Authors: @sindhuhegde , K R Prajwal, Taein Kwon, and Andrew Zisserman
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Understanding Co-speech Gestures in-the-wild (2503.22668)
๐ Web Page: https://www.robots.ox.ac.uk/~vgg/research/jegal
๐ Repository: https://github.com/Sindhu-Hegde/jegal
๐บ Video: https://www.youtube.com/watch?v=TYFOLKfM-rM
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Human Modeling Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/human-modeling.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #CoSpeechGestures #GestureUnderstanding #TriModalRepresentation #MultimodalLearning #AI #ICCV2025 #ResearchHighlight
DmitryRyuminย
posted an update 9 months ago
Post
3990
๐๐ก๐ New Research Alert - ICCV 2025 (Oral)! ๐๐ช๐
๐ Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models ๐
๐ Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM.
๐ฅ Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models (2504.14032)
๐ Github Page: https://andrehuang.github.io/loftup-site
๐ Repository: https://github.com/andrehuang/loftup
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight
๐ Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models ๐
๐ Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM.
๐ฅ Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models (2504.14032)
๐ Github Page: https://andrehuang.github.io/loftup-site
๐ Repository: https://github.com/andrehuang/loftup
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight
DmitryRyuminย
posted an update 9 months ago
Post
1970
๐๐ท๏ธ๐ New Research Alert - ICCV 2025 (Oral)! ๐๐งฉ๐
๐ Title: Heavy Labels Out! Dataset Distillation with Label Space Lightening ๐
๐ Description: The HeLlO framework is a new corpus distillation method that removes the need for large soft labels. It uses a lightweight, online image-to-label projector based on CLIP. This projector has been adapted using LoRA-style, parameter-efficient tuning. It has also been initialized with text embeddings.
๐ฅ Authors: @roseannelexie , @Huage001 , Zigeng Chen, Jingwen Ye, and Xinchao Wang
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Heavy Labels Out! Dataset Distillation with Label Space Lightening (2408.08201)
๐บ Video: https://www.youtube.com/watch?v=kAyK_3wskgA
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #DatasetDistillation #LabelCompression #CLIP #LoRA #EfficientAI #FoundationModels #AI #ICCV2025 #ResearchHighlight
๐ Title: Heavy Labels Out! Dataset Distillation with Label Space Lightening ๐
๐ Description: The HeLlO framework is a new corpus distillation method that removes the need for large soft labels. It uses a lightweight, online image-to-label projector based on CLIP. This projector has been adapted using LoRA-style, parameter-efficient tuning. It has also been initialized with text embeddings.
๐ฅ Authors: @roseannelexie , @Huage001 , Zigeng Chen, Jingwen Ye, and Xinchao Wang
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Heavy Labels Out! Dataset Distillation with Label Space Lightening (2408.08201)
๐บ Video: https://www.youtube.com/watch?v=kAyK_3wskgA
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #DatasetDistillation #LabelCompression #CLIP #LoRA #EfficientAI #FoundationModels #AI #ICCV2025 #ResearchHighlight
- 2 replies
DmitryRyuminย
authored a
paper 9 months ago
DmitryRyuminย
posted an update 9 months ago
Post
4829
๐๐ค๐ New Research Alert - ICCV 2025 (Oral)! ๐๐ค๐
๐ Title: Variance-based Pruning for Accelerating and Compressing Trained Networks ๐
๐ Description: The one-shot pruning method efficiently compresses networks, reducing computation and memory usage while retaining almost full performance and requiring minimal fine-tuning.
๐ฅ Authors: Uranik Berisha, Jens Mehnert, and Alexandru Paul Condurache
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Variance-Based Pruning for Accelerating and Compressing Trained Networks (2507.12988)
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #VarianceBasedPruning #NetworkCompression #ModelAcceleration #EfficientDeepLearning #VisionTransformers #AI #ICCV2025 #ResearchHighlight
๐ Title: Variance-based Pruning for Accelerating and Compressing Trained Networks ๐
๐ Description: The one-shot pruning method efficiently compresses networks, reducing computation and memory usage while retaining almost full performance and requiring minimal fine-tuning.
๐ฅ Authors: Uranik Berisha, Jens Mehnert, and Alexandru Paul Condurache
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Variance-Based Pruning for Accelerating and Compressing Trained Networks (2507.12988)
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #VarianceBasedPruning #NetworkCompression #ModelAcceleration #EfficientDeepLearning #VisionTransformers #AI #ICCV2025 #ResearchHighlight
DmitryRyuminย
posted an update 9 months ago
Post
3041
๐๐๏ธ๐ New Research Alert - ICCV 2025 (Oral)! ๐๐๏ธ๐
๐ Title: Token Activation Map to Visually Explain Multimodal LLMs ๐
๐ Description: The Token Activation Map (TAM) is an advanced explainability method for multimodal LLMs. Using causal inference and a Rank Gaussian Filter, TAM reveals token-level interactions and eliminates redundant activations. The result is clearer, high-quality visualizations that enhance understanding of object localization, reasoning and multimodal alignment across models.
๐ฅ Authors: Yi Li, Hualiang Wang, Xinpeng Ding, Haonan Wang, and Xiaomeng Li
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Token Activation Map to Visually Explain Multimodal LLMs (2506.23270)
๐ Repository: https://github.com/xmed-lab/TAM
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Multi-Modal Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/multi-modal-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #TokenActivationMap #TAM #CausalInference #VisualReasoning #Multimodal #Explainability #VisionLanguage #LLM #XAI #AI #ICCV2025 #ResearchHighlight
๐ Title: Token Activation Map to Visually Explain Multimodal LLMs ๐
๐ Description: The Token Activation Map (TAM) is an advanced explainability method for multimodal LLMs. Using causal inference and a Rank Gaussian Filter, TAM reveals token-level interactions and eliminates redundant activations. The result is clearer, high-quality visualizations that enhance understanding of object localization, reasoning and multimodal alignment across models.
๐ฅ Authors: Yi Li, Hualiang Wang, Xinpeng Ding, Haonan Wang, and Xiaomeng Li
๐ Conference: ICCV, 19 โ 23 Oct, 2025 | Honolulu, Hawai'i, USA ๐บ๐ธ
๐ Paper: Token Activation Map to Visually Explain Multimodal LLMs (2506.23270)
๐ Repository: https://github.com/xmed-lab/TAM
๐ ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
๐ Added to the Multi-Modal Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/multi-modal-learning.md
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #TokenActivationMap #TAM #CausalInference #VisualReasoning #Multimodal #Explainability #VisionLanguage #LLM #XAI #AI #ICCV2025 #ResearchHighlight
- 2 replies
DmitryRyuminย
posted an update over 1 year ago
Post
4095
๐๐ญ๐ New Research Alert - WACV 2025 (Avatars Collection)! ๐๐ญ๐
๐ Title: EmoVOCA: Speech-Driven Emotional 3D Talking Heads ๐
๐ Description: EmoVOCA is a data-driven method for generating emotional 3D talking heads by combining speech-driven lip movements with expressive facial dynamics. This method has been developed to overcome the limitations of corpora and to achieve state-of-the-art animation quality.
๐ฅ Authors: @FedeNoce , Claudio Ferrari, and Stefano Berretti
๐ Conference: WACV, 28 Feb โ 4 Mar, 2025 | Arizona, USA ๐บ๐ธ
๐ Paper: https://arxiv.org/abs/2403.12886
๐ Github Page: https://fedenoce.github.io/emovoca/
๐ Repository: https://github.com/miccunifi/EmoVOCA
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #EmoVOCA #3DAnimation #TalkingHeads #SpeechDriven #FacialExpressions #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #WACV2024
๐ Title: EmoVOCA: Speech-Driven Emotional 3D Talking Heads ๐
๐ Description: EmoVOCA is a data-driven method for generating emotional 3D talking heads by combining speech-driven lip movements with expressive facial dynamics. This method has been developed to overcome the limitations of corpora and to achieve state-of-the-art animation quality.
๐ฅ Authors: @FedeNoce , Claudio Ferrari, and Stefano Berretti
๐ Conference: WACV, 28 Feb โ 4 Mar, 2025 | Arizona, USA ๐บ๐ธ
๐ Paper: https://arxiv.org/abs/2403.12886
๐ Github Page: https://fedenoce.github.io/emovoca/
๐ Repository: https://github.com/miccunifi/EmoVOCA
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #EmoVOCA #3DAnimation #TalkingHeads #SpeechDriven #FacialExpressions #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #WACV2024
- 1 reply
DmitryRyuminย
posted an update almost 2 years ago
Post
3078
๐ฅ๐ญ๐ New Research Alert - HeadGAP (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors ๐
๐ Description: HeadGAP introduces a novel method for generating high-fidelity, animatable 3D head avatars from few-shot data, using Gaussian priors and dynamic part-based modelling for personalized and generalizable results.
๐ฅ Authors: @zxz267 , @walsvid , @zhaohu2 , Weiyi Zhang, @hellozhuo , Xu Chang, Yang Zhao, Zheng Lv, Xiaoyuan Zhang, @yongjie-zhang-mail , Guidong Wang, and Lan Xu
๐ Paper: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019)
๐ Github Page: https://headgap.github.io
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #HeadGAP #3DAvatar #FewShotLearning #GaussianPriors #AvatarCreation #3DModeling #MachineLearning #ComputerVision #ComputerGraphics #GenerativeAI #DeepLearning #AI
๐ Title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors ๐
๐ Description: HeadGAP introduces a novel method for generating high-fidelity, animatable 3D head avatars from few-shot data, using Gaussian priors and dynamic part-based modelling for personalized and generalizable results.
๐ฅ Authors: @zxz267 , @walsvid , @zhaohu2 , Weiyi Zhang, @hellozhuo , Xu Chang, Yang Zhao, Zheng Lv, Xiaoyuan Zhang, @yongjie-zhang-mail , Guidong Wang, and Lan Xu
๐ Paper: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019)
๐ Github Page: https://headgap.github.io
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #HeadGAP #3DAvatar #FewShotLearning #GaussianPriors #AvatarCreation #3DModeling #MachineLearning #ComputerVision #ComputerGraphics #GenerativeAI #DeepLearning #AI
DmitryRyuminย
posted an update almost 2 years ago
Post
2161
๐๐บ๐ New Research Alert - ECCV 2024 (Avatars Collection)! ๐๐๐
๐ Title: Expressive Whole-Body 3D Gaussian Avatar ๐
๐ Description: ExAvatar is a model that generates animatable 3D human avatars with facial expressions and hand movements from short monocular videos using a hybrid mesh and 3D Gaussian representation.
๐ฅ Authors: Gyeongsik Moon, Takaaki Shiratori, and @psyth
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
๐ Paper: Expressive Whole-Body 3D Gaussian Avatar (2407.21686)
๐ Github Page: https://mks0601.github.io/ExAvatar
๐ Repository: https://github.com/mks0601/ExAvatar_RELEASE
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #ExAvatar #3DAvatar #FacialExpressions #HandMotions #MonocularVideo #3DModeling #GaussianSplatting #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
๐ Title: Expressive Whole-Body 3D Gaussian Avatar ๐
๐ Description: ExAvatar is a model that generates animatable 3D human avatars with facial expressions and hand movements from short monocular videos using a hybrid mesh and 3D Gaussian representation.
๐ฅ Authors: Gyeongsik Moon, Takaaki Shiratori, and @psyth
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
๐ Paper: Expressive Whole-Body 3D Gaussian Avatar (2407.21686)
๐ Github Page: https://mks0601.github.io/ExAvatar
๐ Repository: https://github.com/mks0601/ExAvatar_RELEASE
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #ExAvatar #3DAvatar #FacialExpressions #HandMotions #MonocularVideo #3DModeling #GaussianSplatting #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
DmitryRyuminย
posted an update almost 2 years ago
Post
1869
๐ฅ๐ญ๐ New Research Alert - ECCV 2024 (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos ๐
๐ Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.
๐ฅ Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
๐ Github Page: https://shad0wta9.github.io/meshavatar-page
๐ Repository: https://github.com/shad0wta9/meshavatar
๐บ Video: https://www.youtube.com/watch?v=Kpbpujkh2iI
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
๐ Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos ๐
๐ Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.
๐ฅ Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
๐ Github Page: https://shad0wta9.github.io/meshavatar-page
๐ Repository: https://github.com/shad0wta9/meshavatar
๐บ Video: https://www.youtube.com/watch?v=Kpbpujkh2iI
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
DmitryRyuminย
posted an update about 2 years ago
Post
2332
๐๐บ๐ New Research Alert - CVPR 2024 (Avatars Collection)! ๐๐๐
๐ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐
๐ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.
๐ฅ Authors: Shaofei Wang, Boลพidar Antiฤ, Andreas Geiger, and Siyu Tang
๐ Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ
๐ Paper: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing (2312.05210)
๐ Github Page: https://neuralbodies.github.io/IntrinsicAvatar/
๐ Repository: https://github.com/taconite/IntrinsicAvatar
๐บ Video: https://www.youtube.com/watch?v=aS8AIxgVXzI
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #IntrinsicAvatar #InverseRendering #MonocularVideos #RayTracing #VolumetricScattering #3DReconstruction #MachineLearning #ComputerVision #DeepLearning #AI #CVPR2024
๐ Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing ๐
๐ Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.
๐ฅ Authors: Shaofei Wang, Boลพidar Antiฤ, Andreas Geiger, and Siyu Tang
๐ Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ
๐ Paper: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing (2312.05210)
๐ Github Page: https://neuralbodies.github.io/IntrinsicAvatar/
๐ Repository: https://github.com/taconite/IntrinsicAvatar
๐บ Video: https://www.youtube.com/watch?v=aS8AIxgVXzI
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #IntrinsicAvatar #InverseRendering #MonocularVideos #RayTracing #VolumetricScattering #3DReconstruction #MachineLearning #ComputerVision #DeepLearning #AI #CVPR2024
DmitryRyuminย
posted an update about 2 years ago
Post
3155
๐ฅ๐ญ๐ New Research Alert - ECCV 2024 (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models ๐
๐ Description: RodinHD generates high-fidelity 3D avatars from portrait images using a novel data scheduling strategy and weight consolidation regularization to capture intricate details such as hairstyles.
๐ฅ Authors: Bowen Zhang, @yiji , @chunyuwang , Ting Zhang, @jiaolong , Yansong Tang, Feng Zhao, Dong Chen, and Baining Guo
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models (2407.06938)
๐ Github Page: https://rodinhd.github.io/
๐ Repository: https://github.com/RodinHD/RodinHD
๐บ Video: https://www.youtube.com/watch?v=ULvHt7dZx-Q
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #RodinHD #3DAvatars #DiffusionModels #HighFidelity #PortraitTo3D #MachineLearning #ComputerVision #DeepLearning #AI #ECCV2024
๐ Title: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models ๐
๐ Description: RodinHD generates high-fidelity 3D avatars from portrait images using a novel data scheduling strategy and weight consolidation regularization to capture intricate details such as hairstyles.
๐ฅ Authors: Bowen Zhang, @yiji , @chunyuwang , Ting Zhang, @jiaolong , Yansong Tang, Feng Zhao, Dong Chen, and Baining Guo
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models (2407.06938)
๐ Github Page: https://rodinhd.github.io/
๐ Repository: https://github.com/RodinHD/RodinHD
๐บ Video: https://www.youtube.com/watch?v=ULvHt7dZx-Q
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #RodinHD #3DAvatars #DiffusionModels #HighFidelity #PortraitTo3D #MachineLearning #ComputerVision #DeepLearning #AI #ECCV2024
DmitryRyuminย
posted an update about 2 years ago
Post
2447
๐ฅ๐ญ๐ New Research Alert - LivePortrait (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ๐
๐ Description: LivePortrait is an efficient video-driven portrait animation framework that uses implicit keypoints and stitching/retargeting modules to generate high-quality, controllable animations from a single source image.
๐ฅ Authors: @cleardusk , Dingyun Zhang, Xiaoqiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang
๐ค Demo: https://huggingface.co/spaces/KwaiVGI/LivePortrait
๐ Paper: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control (2407.03168)
๐ Github Page: https://liveportrait.github.io/
๐ Repository: https://github.com/KwaiVGI/LivePortrait
๐ฅ Model ๐ค: https://huggingface.co/KwaiVGI/LivePortrait
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI
๐ Title: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ๐
๐ Description: LivePortrait is an efficient video-driven portrait animation framework that uses implicit keypoints and stitching/retargeting modules to generate high-quality, controllable animations from a single source image.
๐ฅ Authors: @cleardusk , Dingyun Zhang, Xiaoqiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang
๐ค Demo: https://huggingface.co/spaces/KwaiVGI/LivePortrait
๐ Paper: LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control (2407.03168)
๐ Github Page: https://liveportrait.github.io/
๐ Repository: https://github.com/KwaiVGI/LivePortrait
๐ฅ Model ๐ค: https://huggingface.co/KwaiVGI/LivePortrait
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #LivePortrait #PortraitAnimation #ComputerVision #MachineLearning #DeepLearning #ComputerGraphics #FacialAnimation #GenerativeAI #RealTimeRendering #AI
DmitryRyuminย
posted an update about 2 years ago
Post
2733
๐๐บ๐ New Research Alert (Avatars Collection)! ๐๐๐
๐ Title: Expressive Gaussian Human Avatars from Monocular RGB Video ๐
๐ Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.
๐ฅ Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang
๐ Paper: Expressive Gaussian Human Avatars from Monocular RGB Video (2407.03204)
๐ Github Page: https://evahuman.github.io/
๐ Repository: https://github.com/evahuman/EVA
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #DigitalAvatars #3DModeling #ComputerVision #MonocularVideo #SMPLX #3DGaussians #AvatarExpressiveness #HandTracking #FacialExpressions #AI #MachineLearning
๐ Title: Expressive Gaussian Human Avatars from Monocular RGB Video ๐
๐ Description: The new EVA model enhances the expressiveness of digital avatars by using 3D Gaussians and SMPL-X to capture fine-grained hand and face details from monocular RGB video.
๐ฅ Authors: Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, and Zhangyang Wang
๐ Paper: Expressive Gaussian Human Avatars from Monocular RGB Video (2407.03204)
๐ Github Page: https://evahuman.github.io/
๐ Repository: https://github.com/evahuman/EVA
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #DigitalAvatars #3DModeling #ComputerVision #MonocularVideo #SMPLX #3DGaussians #AvatarExpressiveness #HandTracking #FacialExpressions #AI #MachineLearning
DmitryRyuminย
posted an update about 2 years ago
Post
2071
๐ฅ๐ญ๐ New Research Alert - ECCV 2024 (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture ๐
๐ Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.
๐ฅ Authors: @Dazz1e , Y. Cheng, @Ryan-sjtu , H. Jia, D. Xu, W. Zhu, Y. Yan
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture (2406.00440)
๐ Github Page: https://xuanchenli.github.io/Topo4D/
๐ Repository: https://github.com/XuanchenLi/Topo4D
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #Topo4D #4DHead #3DModeling #4DCapture #FacialAnimation #ComputerGraphics #MachineLearning #HighFidelity #TextureMapping #DynamicMeshes #GaussianSplatting #VisualEffects #ECCV2024
๐ Title: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture ๐
๐ Description: Topo4D is a novel method for automated, high-fidelity 4D head tracking that optimizes dynamic topological meshes and 8K texture maps from multi-view time-series images.
๐ฅ Authors: @Dazz1e , Y. Cheng, @Ryan-sjtu , H. Jia, D. Xu, W. Zhu, Y. Yan
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture (2406.00440)
๐ Github Page: https://xuanchenli.github.io/Topo4D/
๐ Repository: https://github.com/XuanchenLi/Topo4D
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #Topo4D #4DHead #3DModeling #4DCapture #FacialAnimation #ComputerGraphics #MachineLearning #HighFidelity #TextureMapping #DynamicMeshes #GaussianSplatting #VisualEffects #ECCV2024
- 1 reply
DmitryRyuminย
posted an update about 2 years ago
Post
3739
๐๐ญ๐ New Research Alert - Portrait4D-v2 (Avatars Collection)! ๐๐ญ๐
๐ Title: Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer ๐
๐ Description: Portrait4D-v2 is a novel method for one-shot 4D head avatar synthesis using pseudo multi-view videos and a vision transformer backbone, achieving superior performance without relying on 3DMM reconstruction.
๐ฅ Authors: Yu Deng, Duomin Wang, and Baoyuan Wang
๐ Paper: Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer (2403.13570)
๐ GitHub Page: https://yudeng.github.io/Portrait4D-v2/
๐ Repository: https://github.com/YuDeng/Portrait-4D
๐บ Video: https://www.youtube.com/watch?v=5YJY6-wcOJo
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: Portrait4D #4DAvatar #HeadSynthesis #3DModeling #TechInnovation #DeepLearning #ComputerGraphics #ComputerVision #Innovation
๐ Title: Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer ๐
๐ Description: Portrait4D-v2 is a novel method for one-shot 4D head avatar synthesis using pseudo multi-view videos and a vision transformer backbone, achieving superior performance without relying on 3DMM reconstruction.
๐ฅ Authors: Yu Deng, Duomin Wang, and Baoyuan Wang
๐ Paper: Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer (2403.13570)
๐ GitHub Page: https://yudeng.github.io/Portrait4D-v2/
๐ Repository: https://github.com/YuDeng/Portrait-4D
๐บ Video: https://www.youtube.com/watch?v=5YJY6-wcOJo
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: Portrait4D #4DAvatar #HeadSynthesis #3DModeling #TechInnovation #DeepLearning #ComputerGraphics #ComputerVision #Innovation
- 1 reply