TL;DR
A feature-space alignment framework for person-to-person multi-pose virtual try-on, which estimates appearance flow in a latent feature space and uses the learned flow to warp body-part and garment-related representations to improve realism and structural fidelity.
PoseAdapter, a lightweight framework for high-fidelity 2.5D controllable image generation, and a Context-Aware Dual-Stream Representation, to resolve the generative trade-off between strict instance isolation and global coherence.
PIXIE is a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model, inherently robust to lighting and texture variation, while correspondence filtering handles geometric deviations between the model and physical object.
This work presents FunFlow6D, a novel flow matching-based formulation that leverages features from geometric and appearance foundation models for pose estimation, eliminating the need for task-specific encoders supervised on object-scene overlap and reducing supervision requirements and memory overhead.
UniVVT is presented, a unified end-to-end framework that reframes VVT as semantically conditioned video generation, eliminating mask, pose, and warping modules at inference and validating implicit semantic guidance as a simple and effective alternative to fragile geometric preprocessing for end-to-end virtual try-on.
Infrared and visible image fusion is pivotal for robust visual perception across all weather conditions and scenes. Although deep learning-based methods have made notable progress, most either assume pre-aligned inputs or rely on implicit feature-space alignment, which fails to fundamentally address the amplification o...
This work presents a monocular 6D pose estimation approach using hybrid representations and adaptive multi-feature fusion to address challenges of augmented reality-assisted assembly, human–robot collaboration, and quality inspection in intelligent manufacturing.
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