Selected work

Representative Papers

InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging

Apr 03, 2026

This work addresses the highly ill-posed inverse mapping from 3D garments to 2D sewing patterns, which is complicated by geometric–structural coupling induced by wrinkles. To resolve this challenge, the authors propose a two-stage framework that introduces BoxMesh as a structured intermediate representation to explicitly decouple panel intrinsic geometry, stitching topology, and wrinkle deformation. In the first stage, BoxMesh is reconstructed from the 3D garment; in the second, a geometry-driven and semantics-aware autoregressive model parses the BoxMesh into parametric patterns, incorporating physical constraints and supporting variable-length sequence generation. Evaluated on the GarmentCodeData benchmark, the method achieves state-of-the-art performance and demonstrates strong generalization to real-world scans and single-view images.

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HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits

Apr 03, 2026

This work addresses the challenge of reconstructing hair-strand-level 3D hair models from a single portrait image, where maintaining realistic detail in occluded regions remains difficult. The authors reformulate the task as a calibrated multi-view reconstruction problem and introduce, for the first time, 3D priors derived from video generation models. They propose a two-stage hair strand growing algorithm driven by a hybrid implicit field, incorporating a neural orientation extractor trained on sparse real-world annotations. This approach significantly outperforms existing methods in both visible and occluded regions, achieving high-fidelity, geometrically consistent hair reconstructions across diverse hairstyles at the individual strand level.

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Steering Video Diffusion Transformers with Massive Activations

Mar 18, 2026

This work proposes Structured Activation Steering (STAS), a training-free, self-guided method that leverages the structured distribution of massive activations in video diffusion Transformers. By uncovering, for the first time, the hierarchical spatiotemporal patterns of massive activations within temporally chunked latent spaces, STAS selectively modulates activation values at critical positions to significantly enhance both visual quality and temporal coherence of generated videos with minimal computational overhead. Extensive experiments demonstrate consistent improvements across diverse text-to-video diffusion models, highlighting STAS’s efficiency, robustness, and broad applicability without requiring model retraining or architectural modifications.

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Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates

Feb 27, 2026

High-fidelity 3D reconstruction of loose garments from monocular images or videos remains challenging. This work proposes a unified framework for both static and dynamic clothing reconstruction, leveraging an Implicit Sewing Pattern (ISP) in UV space to encode garment shape priors. By integrating a spatiotemporal diffusion mechanism with analytical projection constraints, the method establishes precise correspondences among pixels, UV coordinates, and 3D geometry. At inference time, a guidance strategy is introduced to enforce cross-frame consistency and plausibly complete occluded regions. Experiments demonstrate that the approach generalizes robustly to real-world imagery, significantly outperforming existing methods in reconstructing fine geometric details. Furthermore, the reconstructed garments support downstream applications such as texture editing, garment retargeting, and animation.

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LUIVITON: Learned Universal Interoperable VIrtual Try-ON

Sep 05, 2025

This work addresses the challenge of fully automatic 3D virtual try-on for multi-layered garments on human-like characters with arbitrary poses and diverse anatomies—including cartoon, robotic, and alien forms. We propose a dual-correspondence learning framework that jointly maps both garment and body to an SMPL proxy model. Geometric learning enables precise part-to-whole garment-to-SMPL alignment, while multi-view consistent appearance features—guided by a pre-trained 2D foundation model—steer a diffusion model to achieve unsupervised, high-fidelity body-to-SMPL cross-domain correspondence. To our knowledge, this is the first end-to-end 3D virtual try-on system that is truly general-purpose, customizable, and fully automated—requiring no manual intervention, 2D garment cutouts, or mesh preprocessing. It natively supports non-manifold meshes, complex geometries, and post-hoc adjustments of garment size and material properties. The method exhibits strong generalization across unseen character types and high computational efficiency, making it suitable for real-world deployment.

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Recent publications

Latest Papers

InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging

Apr 03, 2026

This work addresses the highly ill-posed inverse mapping from 3D garments to 2D sewing patterns, which is complicated by geometric–structural coupling induced by wrinkles. To resolve this challenge, the authors propose a two-stage framework that introduces BoxMesh as a structured intermediate representation to explicitly decouple panel intrinsic geometry, stitching topology, and wrinkle deformation. In the first stage, BoxMesh is reconstructed from the 3D garment; in the second, a geometry-driven and semantics-aware autoregressive model parses the BoxMesh into parametric patterns, incorporating physical constraints and supporting variable-length sequence generation. Evaluated on the GarmentCodeData benchmark, the method achieves state-of-the-art performance and demonstrates strong generalization to real-world scans and single-view images.

0 citationsRead paper

HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits

Apr 03, 2026

This work addresses the challenge of reconstructing hair-strand-level 3D hair models from a single portrait image, where maintaining realistic detail in occluded regions remains difficult. The authors reformulate the task as a calibrated multi-view reconstruction problem and introduce, for the first time, 3D priors derived from video generation models. They propose a two-stage hair strand growing algorithm driven by a hybrid implicit field, incorporating a neural orientation extractor trained on sparse real-world annotations. This approach significantly outperforms existing methods in both visible and occluded regions, achieving high-fidelity, geometrically consistent hair reconstructions across diverse hairstyles at the individual strand level.

0 citationsRead paper

Steering Video Diffusion Transformers with Massive Activations

Mar 18, 2026

This work proposes Structured Activation Steering (STAS), a training-free, self-guided method that leverages the structured distribution of massive activations in video diffusion Transformers. By uncovering, for the first time, the hierarchical spatiotemporal patterns of massive activations within temporally chunked latent spaces, STAS selectively modulates activation values at critical positions to significantly enhance both visual quality and temporal coherence of generated videos with minimal computational overhead. Extensive experiments demonstrate consistent improvements across diverse text-to-video diffusion models, highlighting STAS’s efficiency, robustness, and broad applicability without requiring model retraining or architectural modifications.

0 citationsRead paper

Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates

Feb 27, 2026

High-fidelity 3D reconstruction of loose garments from monocular images or videos remains challenging. This work proposes a unified framework for both static and dynamic clothing reconstruction, leveraging an Implicit Sewing Pattern (ISP) in UV space to encode garment shape priors. By integrating a spatiotemporal diffusion mechanism with analytical projection constraints, the method establishes precise correspondences among pixels, UV coordinates, and 3D geometry. At inference time, a guidance strategy is introduced to enforce cross-frame consistency and plausibly complete occluded regions. Experiments demonstrate that the approach generalizes robustly to real-world imagery, significantly outperforming existing methods in reconstructing fine geometric details. Furthermore, the reconstructed garments support downstream applications such as texture editing, garment retargeting, and animation.

0 citationsRead paper

LUIVITON: Learned Universal Interoperable VIrtual Try-ON

Sep 05, 2025

This work addresses the challenge of fully automatic 3D virtual try-on for multi-layered garments on human-like characters with arbitrary poses and diverse anatomies—including cartoon, robotic, and alien forms. We propose a dual-correspondence learning framework that jointly maps both garment and body to an SMPL proxy model. Geometric learning enables precise part-to-whole garment-to-SMPL alignment, while multi-view consistent appearance features—guided by a pre-trained 2D foundation model—steer a diffusion model to achieve unsupervised, high-fidelity body-to-SMPL cross-domain correspondence. To our knowledge, this is the first end-to-end 3D virtual try-on system that is truly general-purpose, customizable, and fully automated—requiring no manual intervention, 2D garment cutouts, or mesh preprocessing. It natively supports non-manifold meshes, complex geometries, and post-hoc adjustments of garment size and material properties. The method exhibits strong generalization across unseen character types and high computational efficiency, making it suitable for real-world deployment.

0 citationsRead paper