LaMo adds self-supervised latent motion priors via a motion drift loss during training and motion prior guidance during sampling to boost physical fidelity in video diffusion models like CogVideoX.
Videoworld 2: Learning transferable knowledge from real-world videos.arXiv preprint arXiv:2602.10102
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Current video models succeed on basic understanding but achieve under 25% success on logically grounded generation and near 0% on interactive generation, exposing gaps in multimodal reasoning.
OmniJigsaw is a self-supervised proxy task that reconstructs shuffled audio-visual clips via joint integration, sample-level selection, and clip-level masking strategies, yielding gains on 15 video, audio, and reasoning benchmarks.
citing papers explorer
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LaMo: Self-Supervised Latent Motion Priors for Physical Realism in Video Generation
LaMo adds self-supervised latent motion priors via a motion drift loss during training and motion prior guidance during sampling to boost physical fidelity in video diffusion models like CogVideoX.
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How Far Are Video Models from True Multimodal Reasoning?
Current video models succeed on basic understanding but achieve under 25% success on logically grounded generation and near 0% on interactive generation, exposing gaps in multimodal reasoning.
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OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
OmniJigsaw is a self-supervised proxy task that reconstructs shuffled audio-visual clips via joint integration, sample-level selection, and clip-level masking strategies, yielding gains on 15 video, audio, and reasoning benchmarks.