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Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning
Yijun Yang, Shenghe Zheng, Wenbo Li +8
Despite the remarkable prowess of Vision-Language Models (VLMs) in general multimodal tasks, they remain fundamentally ``flat'' when reasoning about the physical world. We argue th…
ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training
Xionghao Wu, Yijun Yang, Shiyang Zhou +17
Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect…
Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence
Yanbing Zhang, Bo Wang, Jianhui Liu +9
Current Large Multimodal Models (LMMs) struggle with spatial reasoning tasks requiring viewpoint-dependent understanding, largely because they are confined to a single, static obse…
View-Consistent 3D Scene Editing via Dual-Path Structural Correspondense and Semantic Continuity
Pufan Li, Bi'an Du, Shenghe Zheng +2
Text-driven 3D scene editing has recently attracted increasing attention. Most existing methods follow a render-edit-optimize pipeline, where multi-view images are rendered from a…
FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer
Shenghe Zheng, Minyu Zhang, Tianhao Liu +1
With the growing availability of open-sourced adapters trained on the same diffusion backbone for diverse scenes and objects, combining these pretrained weights enables low-cost cu…
V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration
Shenghe Zheng, Junpeng Jiang, Wenbo Li
Large-scale video generative models are trained on vast and diverse visual data, enabling them to internalize rich structural, semantic, and dynamic priors of the visual world. Whi…
FRISM: Fine-Grained Reasoning Injection via Subspace-Level Model Merging for Vision-Language Models
Chenyu Huang, Peng Ye, Xudong Tan +4
Efficiently enhancing the reasoning capabilities of Vision-Language Models (VLMs) by merging them with Large Reasoning Models (LRMs) has emerged as a promising direction. However,…
Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging
Shenghe Zheng, Hongzhi Wang, Chenyu Huang +5
With more open-source models available for diverse tasks, model merging has gained attention by combining models into one, reducing training, storage, and inference costs. Current…
Seeing Delta Parameters as JPEG Images: Data-Free Delta Compression with Discrete Cosine Transform
Chenyu Huang, Peng Ye, Xiaohui Wang +5
With transformer-based models and the pretrain-finetune paradigm becoming mainstream, the high storage and deployment costs of individual finetuned models on multiple tasks pose cr…
FREE-Merging: Fourier Transform for Efficient Model Merging
Shenghe Zheng, Hongzhi Wang
With the rapid growth of deep learning, there is an increasing availability of open-source models for various tasks. However, single fine-tuned models often fall short of meeting t…