Welcome to the Best of 3DV series, your virtual pass to some of the groundbreaking research, insights, and innovations that defined this year’s conference. Live streaming from the authors to you. View more CV events here.

In this talk, we'll explore image understanding from a material-centric perspective, namely through the lens of material understanding. Materials distinguish themselves by their response to light, which is governed and modelled through physical properties like roughness or gloss - however, understanding such properties is a non-trivial task for current models and network architectures. We'll see how we can select materials similar to a given query material, significantly improve selection fidelity and eventually even venture beyond 2D, to enable selection in the 3D domain.

This paper presents LAPA (Look Around and Pay Attention), a novel end-to-end transformer-based architecture for multi-camera point tracking that integrates appearance-based matching with geometric constraints. Traditional pipelines decouple detection, association, and tracking, leading to error propagation and temporal inconsistency in challenging scenarios. LAPA addresses these limitations by leveraging attention mechanisms to jointly reason across views and time, establishing soft correspondences through a cross-view attention mechanism enhanced with geometric priors. Instead of relying on classical triangulation, we construct 3D point representations via attention-weighted aggregation, inherently accommodating uncertainty and partial observations.

We introduce Gaussian Wardrobe, a novel framework to digitalize compositional 3D neural avatars from multi-view videos. Existing methods for 3D neural avatars typically treat the human body and clothing as an inseparable entity, which fails to capture the dynamics of complex free-form garments and limits the reuse of clothing across different subjects. To overcome these problems, our method decomposes neural avatars into bodies and layers of shape-agnostic neural garments. Our framework learns the geometry and deformations of each garment layer from multi-view videos and normalizes them into a shape-independent space using 3D Gaussians. We demonstrate that these compositional garments contribute to a versatile digital wardrobe, enabling a practical 3D virtual try-on application where clothing can be freely transferred to new subjects.