In text-to-image generation, evaluating how well the generated image matches the prompt is a major challenge. We address this with VQAScore: a SOTA metric that significantly surpasses CLIPScore, PickScore, ImageReward, TIFA, and more! VQAScore works especially well on complex

Our new work explores generating future observations (blue) given the past (gray), by leveraging large-scale pretraining of image diffusion models for video prediction, and conditioning on timestamps & invariant data modalities. w/ Deva Ramanan page: cs.cmu.edu/~tkhurana/dept…

Lidar Panoptic Segmentation (LPS) is crucial for the safe deployment of autonomous vehicles, but it fails to consider realistic open-world environments. Our IJCV paper introduces LPS in the Open World (LiPSOW) to discover novel classes from the open-world! (1/5)

[1/2] 📢 I'll present "FlashTex: Fast Relightable Mesh Texturing with LightControlNet" at #ECCV2024’s Oral Session tomorrow! 🎉 Join me to explore how our generated textures can be properly relit in various lighting environments. ⚡ 📅 Oral: Tue, Oct 1st, 2 PM 📍 Poster: #159

Sharing exciting news from Milan 🇮🇹: VQAScore (ECCV’24) was highlighted as the strongest text-to-image metric in DeepMind’s Imagen3 tech report! Imagen3 also used our GenAI-Bench (CVPR’24 SynData Best Short Paper) to evaluate compositional text-to-image generation. Catch our

🚀 Make Vision Matter in Visual-Question-Answering (VQA)! Introducing NaturalBench, a vision-centric VQA benchmark (NeurIPS'24) that challenges vision-language models with pairs of simple questions about natural imagery. 🌍📸 Here’s what we found after testing 53 models

🎯 Introducing Sparse Attention Vectors (SAVs): A breakthrough method for extracting powerful multimodal features from Large Multimodal Models (LMMs). SAVs enable SOTA performance on discriminative vision-language tasks (classification, safety alignment, etc.)! Links in replies!

[1/6] Recent models like DUSt3R generalize well across viewpoints, but performance drops on aerial-ground pairs. At #CVPR2025, we propose AerialMegaDepth (aerial-megadepth.github.io), a hybrid dataset combining mesh renderings with real ground images (MegaDepth) to bridge this gap.

Fresh GPT‑o3 results on our vision‑centric #NaturalBench (NeurIPS’24) benchmark! 🎯 Its new visual chain‑of‑thought—by “zooming in” on details—cracks questions that still stump GPT‑4o. Yet vision reasoning isn’t solved: o3 can still hallucinate even after a full minute of

📷 Can AI understand camera motion like a cinematographer? Meet CameraBench: a large-scale, expert-annotated dataset for understanding camera motion geometry (e.g., trajectories) and semantics (e.g., scene contexts) in any video – films, games, drone shots, vlogs, etc. Links

Meet MapAnything – a transformer that directly regresses factored metric 3D scene geometry (from images, calibration, poses, or depth) in an end-to-end way. No pipelines, no extra stages. Just 3D geometry & cameras, straight from any type of input, delivering new state-of-the-art

🎉CameraBench has been accepted as a Spotlight (3%) @ NeurIPS 2025. Huge congrats to all collaborators at CMU, MIT-IBM, UMass, Harvard, and Adobe. CameraBench is a large-scale effort that pushes video-language models to reason about the language of camera motion just like

CogNVS was accepted to NeurIPS Conference 2025! 🎉We are releasing the code today for you all to try: 🆕Code: github.com/Kaihua-Chen/co… Paper: arxiv.org/pdf/2507.12646 With CogNVS, we reformulate dynamic novel-view synthesis as a structured inpainting task: (1) we reconstruct input

🏆 Excited to share that BrickGPT (avalovelace1.github.io/BrickGPT/) received the ICCV Best Paper Award! Our first author, Ava Pun, will present it from 1:30 to 1:45 p.m. today in Exhibit Hall III. Huge thanks to all the co-authors Ruixuan Liu Deva Ramanan Changliu Liu Jun-Yan Zhu

Ava Pun Ruixuan Liu Deva Ramanan Changliu Liu Jun-Yan Zhu If you miss the talk or want to dive deeper, please also check out our poster and our interview! Poster Session Details: - Location: Exhibit Hall I #306 - Time: Wed 22 Oct 2:45 p.m. HST — 4:45 p.m Read the interview in ICCV Daily: rsipvision.com/ICCV2025-Wedne…