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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0fe9f10bbfc76ae9bb31787f73157775CVF · CVPR 2026

DynFusion: Rethinking Condition Fusion for Adaptive Multi-Conditional Text-to-Image Generation

Fang, Zheng, Xiang, Lichuan, Cai, Xu, Wang, Bing, Yang, Bo, Wen, Hongkai

原文摘要

Text-to-image diffusion models have achieved remarkable progress, generating visually realistic and semantically coherent images from textual prompts. However, natural language alone lacks the precision required for design-centric applications that demand strict spatial and structural fidelity--particularly when representing complex concepts that integrate multi-level information, such as product or scene design. To address this limitation, controllable diffusion frameworks introduce auxiliary conditions (e.g., depth, edge, or reference images) to guide the generative process. Models like ControlNet and IP-Adapter effectively inject such priors, improving structural or appearance alignment. Yet, real-world design tasks rarely depend on a single type of condition. They often require simultaneous integration of multiple heterogeneous cues--for instance, preserving spatial layout from depth maps, structural outlines from edge maps, and stylistic attributes from reference images. Current approaches either handle only one condition or naively stack multiple ones, resulting in computational inefficiency and conflicting guidance that degrade generation quality. This multi-condition inconsistency forms a critical bottleneck for applying diffusion models to real-world design workflows, motivating our proposed framework. We propose a data-driven adaptive condition fusion mechanism for multi-conditional diffusion. Our method introduces a novel condition adaptation module that dynamically selects and fuses subsets of conditions based on the diffusion timestep, task characteristics, and feature injection position. This adaptive strategy harmonizes diverse structural and appearance priors, achieving controllable yet flexible generation in complex design scenarios. Experiments demonstrate significant improvements in fidelity, consistency, and controllability across multi-condition tasks, establishing a new direction for practical, detail-preserving diffusion-based design generation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:399cc3b340850e4bf7d1bd8fa3fa8d5bCVF · CVPR 2026

OneThinker: All-in-one Reasoning Model for Image and Video

Feng, Kaituo, Zhang, Manyuan, Li, Hongyu, Fan, Kaixuan, Chen, Shuang, Jiang, Yilei, Zheng, Dian, Sun, Peiwen 等 14 位作者

原文摘要

Reinforcement learning (RL) has recently achieved remarkable success in eliciting visual reasoning within Multimodal Large Language Models (MLLMs). However, existing approaches typically train separate models for different tasks and treat image and video reasoning as disjoint domains. This results in limited scalability toward a multimodal reasoning generalist, which restricts practical versatility and hinders potential knowledge sharing across tasks and modalities.To this end, we propose OneThinker, an all-in-one reasoning model that unifies image and video understanding across diverse fundamental visual tasks, including question answering, captioning, spatial and temporal grounding, tracking, and segmentation. To achieve this, we construct the OneThinker-600k training corpus covering all these tasks and employ commercial models for CoT annotation, resulting in OneThinker-SFT-340k for SFT cold start.Moreover, we propose EMA-GRPO to handle reward heterogeneity in multi-task RL by tracking task-wise moving averages of reward standard deviations for balanced optimization. Extensive experiments on diverse visual benchmarks show that OneThinker delivers strong performance on 31 benchmarks, across 10 fundamental visual understanding tasks.Moreover, it exhibits effective knowledge transfer between certain tasks and preliminary zero-shot generalization ability, marking a step toward a unified multimodal reasoning generalist. All code, model, and data are released.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:5a73333281131904645003d8be49c1c2CVF · CVPR 2026

OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation

Li, Han, Peng, Xinyu, Wang, Yaoming, Peng, Zelin, Chen, Xin, Weng, Rongxiang, Wang, Jingang, Cai, Xunliang 等 10 位作者

原文摘要

We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a single decoder-only transformer architecture. OneCAT uniquely eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference, leading to significant efficiency gains, especially for high-resolution image inputs and outputs. This is achieved through a modality-specific Mixture-of-Experts (MoE) design trained with a unified autoregressive (AR) objective, which also natively supports dynamic resolutions. Furthermore, we pioneer to achieve multi-scale visual autoregressive mechanism within the Large Language Model (LLM) with proposed scale-aware adapter (SAA) that drastically reduces decoding latency compared to diffusion-based methods while maintaining state-of-the-art performance. Our findings demonstrate the powerful potential of pure autoregressive modeling as an elegant foundation for unified multimodal intelligence. As a result, OneCAT outperforms existing unified models across benchmarks for multimodal understanding, generation, and editing.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d35a9d5da9dae269b50a34da4658ba3aCVF · CVPR 2026

Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views

Chen, Zhangquan, Zhang, Manyuan, Yu, Xinlei, Luo, Xufang, Sun, Mingze, Pan, Zihao, An, Xiang, Feng, Yan 等 11 位作者

原文摘要

Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limited views remains a significant challenge. Previous reasoning methods typically rely on pure text (e.g., topological cognitive maps) or on 2D visual cues. However, their limited representational capacity hinders performance in specific tasks that require 3D spatial imagination. To address this limitation, we propose 3DThinker, a framework that can effectively exploit the rich geometric information embedded within images while reasoning, like humans do. Our framework is the first to enable 3D mentaling during reasoning without any 3D prior input, and it does not rely on explicitly labeled 3D data for training. Specifically, our training consists of two stages. First, we perform supervised training to align the 3D latent generated by VLM while reasoning with that of a 3D foundation model (e.g., VGGT). Then, we optimize the entire reasoning trajectory solely based on outcome signals, thereby refining the underlying 3D mentaling. Extensive experiments across multiple benchmarks show that 3DThinker consistently outperforms strong baselines and offers a new perspective toward unifying 3D representations into multimodal reasoning. Our code is available at https://github.com/zhangquanchen/3DThinker.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗