Character Animation
Robust Motion Generation Using Part-Level Reliable Data from Videos
This research introduces ROPAR, a novel framework addressing the challenge of generating high-quality human motion from noisy web videos. It decomposes the human body into five parts, identifies 'credible' parts using joint confidence, and uses a part-aware Variational Autoencoder (P-VAE) to encode reliable data. A robust masked autoregression model then predicts full-body motion, ignoring noisy parts, refined by a diffusion head. The method outperforms baselines on both clean and noisy datasets (including a new K700-M benchmark) in terms of motion quality, semantic consistency, and diversity, demonstrating a scalable solution for character animation despite data imperfections.
Executive Impact & Key Metrics
Our analysis reveals quantifiable benefits and advancements delivered by leveraging this research in an enterprise context.
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Improved R-Precision (K700-M)
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Noisy Motion Sequences (K700-M)
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Human Body Parts Decomposed
Deep Analysis & Enterprise Applications
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Existing motion generation methods struggle with the pervasive part-level noise in web-sourced video data due to occlusions or incomplete views. Discarding incomplete data limits scale and diversity, while including it compromises data quality and model performance. This leads to erroneous model distributions and poor generation quality.
ROPAR addresses this by selectively using reliable, part-level reconstructed motion data. It involves: 1) Decomposing the human body into five parts and identifying 'credible' parts using joint confidence scores from ViTPose [17]. 2) Encoding credible parts into latent tokens using a part-aware Variational Autoencoder (P-VAE). 3) A robust part-level masked generation model (transformer with diffusion head) that predicts full-body motion, ignoring noisy parts, significantly enhancing robustness to noise and expanding usable data scope.
A new challenging benchmark, K700-M, is introduced. Curated from Kinetics-700 [10], it comprises approximately 200,000 noisy motion sequences extracted from real-world web videos. This dataset specifically addresses the need for evaluating models on imperfect, part-level motion data, enabling more realistic performance assessment.
Experiments on HumanML3D (clean) and K700-M (noisy) datasets demonstrate that ROPAR significantly outperforms baselines in FID, R-Precision, and MM-Distance. It shows improved motion quality, semantic consistency, and diversity, with stable performance even with varying noise levels. Ablation studies confirm the importance of part-aware decomposition, shared parameters in P-VAE, and the diffusion head.
13.612
Lowest FID on K700-M (Ours)
Enterprise Process Flow
Decompose Body (5 Parts)
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Detect 'Credible' Parts (ViTPose)
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Encode Credible Parts (P-VAE)
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Masked Generation (Transformer)
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Refine Latents (Diffusion Head)
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Full Body Motion Output
| Feature | MotionStreamer (Baseline) | ROPAR (Our Method) |
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Impact of K700-M Dataset
The introduction of the K700-M dataset, derived from Kinetics-700 web videos, highlights the critical need for benchmarks reflecting real-world data imperfections. ROPAR's superior performance on K700-M demonstrates its ability to handle challenges like partial occlusion and incomplete views, a common issue in web-sourced motion data. This validates its potential for widespread application in contexts where perfect motion capture data is scarce.
Key Highlight: ROPAR achieves significant improvements over strong baselines in quality, semantic consistency, and diversity on this challenging noisy dataset.
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