WearAnything: Dress 3D Humans in Seconds via Decoupled Gaussian Garment Transfer

We present WearAnything, a feed-forward framework for direct 3D garment transfer in Gaussian human representations, enabling efficient and controllable virtual try-on without multi-stage reconstruction or subject-specific optimization. Existing 3D virtual try-on methods are computationally expensive and often struggle with cross-category transfer involving substantial changes in garment coverage. WearAnything performs garment transfer directly in the canonical token representation of a pretrained 3D human model. The framework learns separate identity and garment latent spaces from dense canonical features, while a lightweight routing module adapts the editable region to the target garment support. Together, these components enable the recomposition of source identities and target garments across clothing categories, including transfers with substantial changes in garment coverage. To maintain 3D structure during garment editing, we introduce a 3D distillation strategy that transfers canonical feature and Gaussian geometry supervision from the frozen reconstruction model to the editable representation. The learned garment space further supports continuous outfit interpolation. Experiments demonstrate state-of-the-art 3D garment transfer quality and fast end-to-end inference within a few seconds.