VIPER: Visual In-Context Physics Reasoning for Physically Plausible Video Generation

Modern video generation models can synthesize visually compelling and temporally coherent clips, yet controlling their physical behavior remains difficult with standard text and image conditions. The core challenge is a conditioning bottleneck: material response, contact interaction, deformation, and motion trajectory are continuous and relational physical cues that are hard to specify exhaustively in language but can be demonstrated naturally by video. We propose VIPER, a Visual In-Context Physics Reasoning framework for reference-guided image-to-video generation. Given a target image, a brief target prompt, and a reference video, VIPER treats the reference as a dense visual demonstration of the desired physical process rather than an appearance template. It uses a Multimodal Large Language Model (MLLM) to extract reference-derived physical cues and guide a pretrained image-to-video generator through a hierarchical training strategy, enabling physical behavior transfer while preserving the visual prior of the base generator. To support this setting, we construct VIPER-19K, a curated dataset with material, trajectory, and physical-impact annotations, together with filtered reference-target pairs. Experiments on an unseen validation set show that VIPER achieves stronger reference-video physical similarity and higher human preference than representative video generation and video-as-prompt baselines, while maintaining competitive general video quality. Qualitative results further demonstrate that VIPER can transfer reference-derived physical behavior to new target scenes without requiring carefully engineered prompts.

2L3: Lifting Imperfect Generated 2D Images into Accurate 3D

Reconstructing 3D objects from a single image is an intriguing but challenging problem. One promising solution is to utilize multi-view (MV) 3D reconstruction to fuse generated MV images into consistent 3D objects. However, the generated images usually suffer from inconsistent lighting, misaligned geometry, and sparse views, leading to poor reconstruction quality. To cope with these problems, we present a novel 3D reconstruction framework that leverages intrinsic decomposition guidance, transient-mono prior guidance, and view augmentation to cope with the three issues, respectively. Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures. Our approach, therefore, enables the integration of a pre-trained MV image generator and a neural network-based volumetric signed distance function (SDF) representation for a single image to 3D object reconstruction. We evaluate our framework on various datasets and demonstrate its superior performance in both quantitative and qualitative assessments, signifying a significant advancement in 3D object reconstruction. Compared with the latest state-of-the-art method Syncdreamer~\cite{liu2023syncdreamer}, we reduce the Chamfer Distance error by about 36\% and improve PSNR by about 30\% .

Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification

Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks. Typically, prompt learning relies on a fixed prompt token or an input-conditional token to fit a small amount of data under full supervision. While this paradigm can generalize to a certain range of unseen classes, it may struggle when domain gap increases, such as in fine-grained classification and satellite image segmentation. To address this limitation, we propose Retrieval-enhanced Prompt learning (RePrompt), which introduces retrieval mechanisms to cache the knowledge representations from downstream tasks. we first construct a retrieval database from training examples, or from external examples when available. We then integrate this retrieval-enhanced mechanism into various stages of a simple prompt learning baseline. By referencing similar samples in the training set, the enhanced model is better able to adapt to new tasks with few samples. Our extensive experiments over 15 vision datasets, including 11 downstream tasks with few-shot setting and 4 domain generalization benchmarks, demonstrate that RePrompt achieves considerably improved performance. Our proposed approach provides a promising solution to the challenges faced by prompt learning when domain gap increases. The code and models will be available.