papers are kind of like movies: the first one is usually the best, and the sequels tend to get more complicated but not really more exciting. But that totally doesn’t apply to the DepthAnything series. @bingyikang's team somehow keeps making things simpler and more scalable each

After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3

Thank you for sharing our work! Marigold is really cool! However, it’s somewhat limited by the image VAE — many flying points appear just after encoding a perfect ground-truth depth. Pixel-space diffusion to the rescue 🚀

Pixel-Perfect-Depth: the paper aims to fix Marigold's loss of sharpness induced by VAE by using VFMs (VGGT/DAv2) and a DiT-based pixel decoder to refine the predictions and achieve clean depth discontinuities. Video by authors.

🚀 We release SpatialTrackerV2: the first feedforward model for dynamic 3D reconstruction and 3D point tracking — all at once! Reconstruct dynamic scenes and predict pixel-wise 3D motion in seconds. 🔗 Webpage: https://t.co/B8widtJ6DT 🔍 Online Demo: https://t.co/sY9iO7wCgT

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation @realzhenxu, Hongyu Zhou, @pengsida, @HaotongLin, @ghy990324, @jiahaoshao1, Peishan Yang, Qinglin Yang, Sheng Miao, @XingyiHe1, Yifan Wang, Yue Wang, @ruizhen_hu, @yiyi_liao_, @XiaoweiZhou5, Hujun Bao

Wow, thank you for crediting our work! Thrilled to see our project PromptDepthAnything being used in your latest release. This is awesome! Best of luck with the new version! !

Recently, I've been playing with my iPhone ToF sensor, but the problem has always been the abysmal resolution (256x192). The team behind DepthAnything released PromptDepthAnything that fixes this. Using @Polycam3D to collect the raw data, @Gradio to generate a UI, and

Excited to share our work MatchAnything: We pre-train strong universal image matching models that exhibit remarkable generalizability on unseen multi-modality matching and registration tasks. Project page: https://t.co/o5GisUJ7RT Huggingface Demo: https://t.co/qbz33QBulI

(2/2) Something interesting I found is that recent monocular depth methods like Depth Pro can reconstruct highly detailed depth, but these depths are inconsistent in 3D, leading to poor reconstruction. Instead, our approach with low-cost LiDAR guidance yields 3D-consistent depth.

Check out our new work, Prompt Depth Anything, which achieves accurate metric depth estimation at up to 4K resolution! Thanks to all our collaborators!

Want to use Depth Anything, but need metric depth rather than relative depth? Thrilled to introduce Prompt Depth Anything, a new paradigm for accurate metric depth estimation with up to 4K resolution. 👉Key Message: Depth foundation models like DA have already internalized rich

(1/8) Ever wanted to create an avatar of yourself that interacts realistically with different lighting? In our CVPR 2024 Highlight🌟paper, we present a method for creating relightable and animatable avatars from only sparse/monocular video. Project Page: https://t.co/95CmyibzIh

A really cool project!

Excited to unveil Im4D at #SIGGRAPHAsia2023! Im4D: High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes; Visit our project page to explore more cool demos! https://t.co/PsGEEoQxxI

https://t.co/hXB00p0L7o

Photorealistic rendering of dynamic scenes at interactive frame rates. Check our SIGGRAPH Asia 2022 paper: Efficient Neural Radiance Fields for Interactive Free-viewpoint Video Project page: https://t.co/yfNSBygsoL Github: https://t.co/lhlqNnt7tC