TryOnCrafter is the first DiT-based framework for camera-controllable video virtual try-on via a renderable 4D try-on proxy distilled from 2D priors into 3DGS avatar animated with SMPL-X.
Dreamvvt: Mastering realistic video virtual try-on in the wild via a stage-wise diffusion transformer framework
6 Pith papers cite this work. Polarity classification is still indexing.
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OmniTryOn performs multi-object video virtual try-on in one pass using first-frame wearable caching and spatiotemporal RoPE, outperforming single-garment baselines on a new TryAny-Bench dataset.
A new large-scale triplet dataset and diffusion transformer model using coarse human masks deliver improved video virtual try-on quality and generalization in challenging real-world conditions.
KeyTailor improves video virtual try-on realism by using instruction-guided keyframes to enhance garment details and background integrity in DiT models without major architectural changes.
A new dataset with high-fidelity close-up garment images and full/close-up try-on videos plus the VGID metric enables better texture and structure preservation in high-resolution video virtual try-on.
A survey that organizes diffusion image-to-video methods into a taxonomy, distills core designs in condition encoding, temporal modeling, noise prior, and upsampling, and discusses applications plus challenges.
citing papers explorer
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TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy
TryOnCrafter is the first DiT-based framework for camera-controllable video virtual try-on via a renderable 4D try-on proxy distilled from 2D priors into 3DGS avatar animated with SMPL-X.
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OmniTryOn: Video Try-On Anything at Once!
OmniTryOn performs multi-object video virtual try-on in one pass using first-frame wearable caching and spatiotemporal RoPE, outperforming single-garment baselines on a new TryAny-Bench dataset.
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TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On
A new large-scale triplet dataset and diffusion transformer model using coarse human masks deliver improved video virtual try-on quality and generalization in challenging real-world conditions.
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The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection
KeyTailor improves video virtual try-on realism by using instruction-guided keyframes to enhance garment details and background integrity in DiT models without major architectural changes.
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Eevee: Towards Close-up High-resolution Video-based Virtual Try-on
A new dataset with high-fidelity close-up garment images and full/close-up try-on videos plus the VGID metric enables better texture and structure preservation in high-resolution video virtual try-on.
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Image-to-Video Diffusion: From Foundations to Open Frontiers
A survey that organizes diffusion image-to-video methods into a taxonomy, distills core designs in condition encoding, temporal modeling, noise prior, and upsampling, and discusses applications plus challenges.