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20202026
most citedGET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

187 citations · 294 across the 31 of their papers we have counts for

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14 papers · 1 filter

cs.CV2026

PixelUMM: Encoder-Free Unified Image and Video Understanding and Generation

Cong Wei, Xuanchi Ren, Bryan Chu +8

Unified Multimodal Models (UMMs) often rely on separate visual representations for understanding and generation, increasing visual context length and complicating integration with…

cs.CV2026

Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation

Hmrishav Bandyopadhyay, Xuanchi Ren, Zijian Huang +7

Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps,…

cs.CV2026

NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation

NVIDIA, :, Aarti Basant +33

As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…

cs.CV2026

Beyond Scene Priors: Fine-Grained Traffic Scene Reasoning with Benchmarking and Query-Guided Small-Object Focus

Waikit Xiu, Qiang Lu, Zian Wang +4

In safety-critical traffic scenarios, answering complex questions relies on minute, localized visual cues. However, standard Multimodal Large Language Models (MLLMs) tend to over-a…

cs.CV2026

HorizonRelight: Relighting Long-horizon Videos Consistently via Diffusion Transformers

Jing Yang, Mayoore Jaiswal, Zian Wang +4

Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short clips and applied to long-hori…

cs.CV2026

Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation

Siyi Chen, Shaowei Liu, Yixuan Jia +4

Recent progress has shown promise in distilling multi-step video diffusion models into efficient few-step students. Among them, Distribution Matching Distillation (DMD) and its suc…

cs.CV2026

TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructions

Or Perel, Hassan Abu Alhaija, Zian Wang +4

We introduce TRON, a rendering framework that combines 3D Gaussian ray tracing with neural rendering to enable realistic and controllable rendering of real-world 3D scenes under no…

cs.CV2026

APE: Agentic Prompt Enhancer for Image Generation and Editing

Zijian Huang, Jay Zhangjie Wu, Zian Wang +5

Natural language has become a powerful interface for image generation and editing, yet text-guided visual systems remain highly sensitive to prompt formulation. Semantically simila…

cs.CV2026

Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players

Fangfu Liu, Kai He, Tianchang Shen +7

World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many gen…

eess.SY2026

DRIFT: Driving Risk Inference via Field Transmission for Human-like Autonomous Driving

Zian Wang, Yiming Shu, Zejian Deng +1

Risk fields offer spatially structured alternatives to scalar safety metrics. However, hand-crafted static risk field models struggle with occlusion and topology-driven propagation…

cs.CV2026

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion

Yifan Lu, Qi Wu, Jay Zhangjie Wu +4

Most practical high-resolution text-to-image systems, including latent diffusion and autoregressive models, perform generation in a compact latent space, and a decoder maps the gen…

cs.CV2026

The Fourth Challenge on Image Super-Resolution (4) at NTIRE 2026: Benchmark Results and Method Overview

Zheng Chen, Kai Liu, Jingkai Wang +150

This paper presents the NTIRE 2026 image super-resolution (4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to r…

cs.CV2026

Lyra 2.0: Explorable Generative 3D Worlds

Tianchang Shen, Sherwin Bahmani, Kai He +12

Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via f…

cs.CV2026

DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer

Yuxuan Zhang, Katarína Tóthová, Zian Wang +7

Simulation is essential to the development and evaluation of autonomous robots such as self-driving vehicles. Neural reconstruction is emerging as a promising solution as it enable…