Computer Vision - ECCV 2024

Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning.- Nuvo: Neural UV Mapping for Unruly 3D Representations.- Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation.- AnyHome: Open-Vocabulary Large-Scale Indoor Scene Generation with First-Person View Exploration.- Better Call SAL: Towards Learning to Segment Anything in Lidar.- DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control.- DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement.- Scene-aware Human Motion Forecasting via Mutual Distance Prediction.- FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting.- Open Panoramic Segmentation.- iMatching: Imperative Correspondence Learning.- COSMU: Complete 3D human shape from monocular unconstrained images.- MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps.- Appearance-based Refinement for Object-Centric Motion Segmentation.- SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance.- Open Vocabulary Multi-Label Video Classification.- Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data.- Regularizing Dynamic Radiance Fields with Kinematic Fields.- MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation.- Efficient Pre-training for Localized Instruction Generation of Procedural Videos.- MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution.- DEAL: Disentangle and Localize Concept-level Explanations for VLMs.- Fast Encoding and Decoding for Implicit Video Representation.- Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models.- Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following.- IMMA: Immunizing text-to-image Models against Malicious Adaptation.- Motion-Oriented Compositional Neural Radiance Fields for Monocular Dynamic Human Modeling.