TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy
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Abstract (arXiv)
While Video Virtual Try-on (VVT) has achieved remarkable progress in synthesizing realistic garment overlays on dynamic subjects, existing paradigms remains fundamentally constrained by a passive dependency on source camera trajectories, failing to accommodate the requisite interactive freedom for omnidirectional viewpoint exploration. To address this limitation, we define a pioneering research frontier: Camera-controllable Video Virtual Try-on (CaM-VVT). Unlike conventional VVT, CaM-VVT not only necessitates viewpoint-agnostic texture hallucination but also strict structural synchronization between non-rigid human dynamics and background contexts under arbitrary, unconstrained camera movements. To tackle these challenges, we present TryOnCrafter, the first unified DiT-based framework specifically architected for the CaM-VVT task. Departing from implicit pixel-space manipulation, we introduce a Renderable 4D Try-on Proxy that explicitly decouples the human subject from the environment. This is achieved by distilling high-fidelity 2D try-on priors into a clothed 3DGS-based avatar, which is subsequently animated via SMPL-X sequences and metric-aligned into a reconstructed background point cloud. This proxy establishes a robust structural foundation with superior texture density and motion integrity. Our Proxy-Anchored Video DiT leverages this robust structural foundation as a primary geometric anchor, ensuring that the synthesized photorealistic videos are strictly constrained by prescribed trajectories and physically plausible deformations. Benefiting from the inherent editability of the 4D proxy, TryOnCrafter facilitates diverse downstream applications, including human relocalization, ``bullet time'' effects, and 360-degree orbital viewing.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 49 of its 54 references that are in the map. First posted on arXiv 24 Jun 2026.
Builds on
49 of 54 references.
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37 more
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Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generationcited by 2
- 2026ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesiscited by 1
- 2026TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioningcited by 0
- 2026TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioning30 Sep 2026
- 2026From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation18 Aug 2026
- 2026ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis21 Jul 2026