OVOW reconstructs instance-level, simulation-ready 4D mesh scenes from monocular video via a four-stage training-free pipeline and introduces a new benchmark for structured Video-to-4D evaluation.
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ViPS learns a universal, controllable pose space for auto-rigged meshes by transferring motion priors from video diffusion models, matching SOTA performance on plausibility and diversity while enabling zero-shot generalization.
MoZoo generates high-fidelity animal videos with fur and muscle dynamics from coarse meshes by extending video diffusion with role-aware RoPE and asymmetric decoupled attention, trained on a new synthetic-to-real dataset.
A single freehand sketch can generate a full orbit of photorealistic views in one pass, trained on a 9k synthetic sketch-to-multiview dataset with camera-aware adapters and SfM-supervised correspondences.
Dress-ED is a 146k verified quadruplet dataset that unifies instruction-driven virtual try-on, try-off, and garment editing, with a multimodal diffusion baseline.
citing papers explorer
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One Video, One World: Turning Monocular Video into Physical 4D Scenes
OVOW reconstructs instance-level, simulation-ready 4D mesh scenes from monocular video via a four-stage training-free pipeline and introduces a new benchmark for structured Video-to-4D evaluation.
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ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes
ViPS learns a universal, controllable pose space for auto-rigged meshes by transferring motion priors from video diffusion models, matching SOTA performance on plausibility and diversity while enabling zero-shot generalization.
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MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation
MoZoo generates high-fidelity animal videos with fur and muscle dynamics from coarse meshes by extending video diffusion with role-aware RoPE and asymmetric decoupled attention, trained on a new synthetic-to-real dataset.
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Geometrically Consistent Multi-View Scene Generation from Freehand Sketches
A single freehand sketch can generate a full orbit of photorealistic views in one pass, trained on a 9k synthetic sketch-to-multiview dataset with camera-aware adapters and SfM-supervised correspondences.
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Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off
Dress-ED is a 146k verified quadruplet dataset that unifies instruction-driven virtual try-on, try-off, and garment editing, with a multimodal diffusion baseline.