#motion-generation News & Analysis

3 articles tagged with #motion-generation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · May 297/10

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AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

Researchers introduce AnyMo, a unified framework for conditional human motion generation that supports arbitrary modality combinations (text, speech, music, trajectory). The work is enabled by OmniHuMo, a large-scale dataset of 5,000+ hours of motion with precisely aligned multimodal annotations, addressing the critical bottleneck of training data scarcity in multimodal synthesis.

AINeutralarXiv – CS AI · Jun 46/10

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Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models

Researchers identify reference-frame dominance as the cause of static motion in image-to-video models and propose DyMoS, a training-free method that rebalances attention mechanisms to improve motion dynamics while preserving image fidelity. The approach requires no model retraining and introduces a single controllable parameter for motion strength adjustment.

AIBullisharXiv – CS AI · Mar 36/103

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Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation

Sketch2Colab is a new AI system that converts 2D sketches into realistic 3D multi-human animations with precise control over interactions and movements. The technology uses a novel approach combining sketch-driven diffusion with rectified-flow distillation for faster, more stable animation generation than existing methods.