arXiv AI
Robust Motion Generation using Part-level Reliable Data from Videos
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arXiv:2608.23279v1 Announce Type: new Abstract: Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For rep...
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WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation
arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
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Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning
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BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data
BooM‑VVT is a mask‑free video virtual try‑on framework that builds on a keyframe‑driven paradigm. It introduces a multi‑stage training strategy using image‑level pseudo data to learn mask‑free localization, a garment‑sensitive keyframe sampling method to capture garment appearance, and a Frame‑Shared 3D‑RoPE module to align keyframes with target video frames for accurate garment detail transfer. The authors also release OmniView, a large‑scale multi‑view try‑on dataset, and demonstrate that BooM‑VVT outperforms existing methods in temporal consistency and garment fidelity.
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BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pseudo Data
BooM-VVT is a mask‑free video virtual try‑on framework that builds on a keyframe‑driven paradigm. It uses a multi‑stage training strategy with image‑level pseudo data to learn mask‑free localization, introduces Garment‑Sensitive Keyframe Sampling to capture garment appearance, and employs Frame‑Shared 3D‑RoPE for spatiotemporal correspondence. The authors also create the OmniView dataset to support diverse camera viewpoints and tasks, achieving superior temporal consistency and garment fidelity compared to existing methods.