arXiv Computer Vision
Strike a Chord! Modal Kinetic Typography
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arXiv Computer Vision
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Beyond Legibility: Benchmarking Visual Text Rendering and In-Place Editing in Unified Video Generation
arXiv:2609.36598v1 Announce Type: new Abstract: A video can exhibit convincing motion and photorealism yet fail immediately when visual text collapses. Unlike generic scene content, visual text is un...
arXiv Computer Vision
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Motion Concept Unlearning in Video Diffusion Models
arXiv:2609.36832v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that...
arXiv Computer Vision
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MorphoStyle: Motion Style Transfer with Morphology Control
MorphoStyle is a new framework for shape‑aware motion style transfer that uses a shape‑conditioned FSQ‑VAE. It disentangles style from content through a contrastive style encoder, a text‑guided style‑routing mechanism, and a manifold‑preserving style modulator. Experiments on benchmark datasets show that MorphoStyle outperforms existing baselines in both shape control and motion style transfer.
arXiv Computer Vision
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Multi-Modal Controlled Coherent Motion Generation
The paper introduces MOCO, a diffusion-based framework that generates 3D avatar motions from concurrent multimodal inputs such as speech audio, text descriptions, and trajectory data. MOCO decouples motion generation by independently producing modality-specific motions at each denoising step and then assembling them according to spatial rules, iteratively refining the combined motion. This approach yields coherent, lifelike, and synchronized movements, outperforming existing baselines on a multimodal benchmark.
arXiv Machine Learning
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Flexible Motion Generation from Language and Style References
arXiv:2609.08032v1 Announce Type: cross Abstract: We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style refe...
arXiv AI
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Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
arXiv:2606. 22726v2 Announce Type: replace Abstract: Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressive full-body dynamics.