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FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions.- ScanTalk: 3D Talking Heads from Unregistered Scans.- Controllable Navigation Instruction Generation with Chain of Thought Prompting.- GiT: Towards Generalist Vision Transformer through Universal Language Interface.- ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attention.- A Cephalometric Landmark Regression Method based on Dual-encoder for High-resolution X-ray Image.- Exploring the Feature Extraction and Relation Modeling For Light-Weight Transformer Tracking.- LiveHPS++: Robust and Coherent Motion Capture in Dynamic Free Environment.- You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation.- Gaussian Grouping: Segment and Edit Anything in 3D Scenes.- CoMo: Controllable Motion Generation through Language Guided Pose Code Editing.- MegaScenes: Scene-Level View Synthesis at Scale.- SuperGaussian: Repurposing Video Models for 3D Super Resolution.- Towards Model-Agnostic Dataset Condensation by Heterogeneous Models.- Goldfish: Vision-Language Understanding of Arbitrarily Long Videos.- MeshFeat: Multi-Resolution Features for Neural Fields on Meshes.- Decoupling Common and Unique Representations for Multimodal Self-supervised Learning.- MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.- Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation.- 2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion Correction.- Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models.- D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction.- Combining Generative and Geometry Priors for Wide-Angle Portrait Correction.- RealViformer: Investigating Attention for Real-World Video Super-Resolution.- Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution.- Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation.- UniFS: Universal Few-shot Instance Perception with Point Representations.