Guandao Yang @stevenygd
Building spatial intelligence that can generate and understand 3D. guandaoyang.com Palo Alto Joined March 2015-
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We've released all code and models for FlashDepth! It produces depth maps from a 2k, streaming video in real-time. This was a really fun course project inspired by discussions with @mohsaied and @stevenygd and we look forward to presenting it at #ICCV2025. GitHub:…
We've released all code and models for FlashDepth! It produces depth maps from a 2k, streaming video in real-time. This was a really fun course project inspired by discussions with @mohsaied and @stevenygd and we look forward to presenting it at #ICCV2025. GitHub:… https://t.co/p7hJUY5J7S
For those at CVPR, Aditya will be presenting this poster tomorrow at 10:30 (Exhibit hall D, Poster #34). Come hear about why neural field derivatives are noisy, and how we resurrect image processing ideas for neural fields!
For those at CVPR, Aditya will be presenting this poster tomorrow at 10:30 (Exhibit hall D, Poster #34). Come hear about why neural field derivatives are noisy, and how we resurrect image processing ideas for neural fields!
Really impressive work on real-time video generation! I’m a fan of the principle of closing the train-test gap!
Really impressive work on real-time video generation! I’m a fan of the principle of closing the train-test gap!
Most video models struggle to feel like real worlds. They forget what’s just out of view, slow down as videos get longer, or breaks causality. We think State Space Models are a natural fit for models with: 🧠 long-term memory across hundreds of frames ⚡ constant-speed…
Most video models struggle to feel like real worlds. They forget what’s just out of view, slow down as videos get longer, or breaks causality. We think State Space Models are a natural fit for models with: 🧠 long-term memory across hundreds of frames ⚡ constant-speed… https://t.co/TOI8jX8ogI
Introducing AIpparel @CVPR 2025 - the first multimodal foundation model for digital garments. georgenakayama.github.io/AIpparel/ 1/6
How can we use wide-FOV cameras for reconstruction? We propose self-calibration Gaussian Splatting that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations to directly reconstruct from a set of wide-angle captures. page: denghilbert.github.io/self-cali/
Do large multimodal models understand how to make dresses for your winter holiday party💃? We introduce AIpparel, a vision-language-garment model capable of generating and editing simulation-ready sewing patterns from text and images. Project page at georgenakayama.github.io/AIpparel/.…
🚀 Introducing CausVid: Instant video generation that plays the moment you hit "Generate", while maintaining state-of-the-art quality! Project Page: causvid.github.io. More details in the long thread.
[Hiring!] I am hiring multiple PhDs @CSatUSC @USCViterbi for this cycle. If you're interested in scene representations, neural simulation, generative AI, and robotics, feel free to mention my name in your application (no need to email). For USC masters/undergrads who're…
A common question nowadays: Which is better, diffusion or flow matching? 🤔 Our answer: They’re two sides of the same coin. We wrote a blog post to show how diffusion models and Gaussian flow matching are equivalent. That’s great: It means you can use them interchangeably.
Sharing something exciting we've been working on as a Thanksgiving gift: Diffusion Self-Distillation (DSD), which redefines zero-shot customized image generation using FLUX. DSD is like DreamBooth, but zero-shot/training-free. It works across any input subject and desired…
Dynamic 3D scenes in your browser 🤯 Powered by @ArthurBrussee's amazing Brush rendering engine (github.com/ArthurBrussee/…)
Dynamic 3D scenes in your browser 🤯 Powered by @ArthurBrussee's amazing Brush rendering engine (github.com/ArthurBrussee/…)
I’m recruiting PhD students with computer vision, robotics, or ML experience! We especially encourage applicants from physics and related fields who want to explore AI for Science. Join us by applying to @UMichCSE's PhD program!
We've released our paper "Generating 3D-Consistent Videos from Unposed Internet Photos"! Video models like Luma generate pretty videos, but sometimes struggle with 3D consistency. We can do better by scaling them with 3D-aware objectives. 1/N page: genechou.com/kfcw
Curious whether video generation models (like #SORA) qualify as world models? We conduct a systematic study to answer this question by investigating whether a video gen model is able to learn physical laws. Three are three key messages to take home: 1⃣The model generalises…
There has been significant recent interest in methods that use random walks to solve PDEs. In a project to be presented at SIGGRAPH Asia (w/Ekrem Yılmazer and @DelioVicini), we investigated how to solve *inverse PDE* problems by differentiating such solvers.
Detecting symmetries, particularly in noisy data, is challenging. Our method uses Langevin dynamics to make spotting symmetry easier, even in complex and noisy real-world data!
Detecting symmetries, particularly in noisy data, is challenging. Our method uses Langevin dynamics to make spotting symmetry easier, even in complex and noisy real-world data!
Solving PDEs with limited data and system observations is a critical yet often overlooked challenge. Our #NeurIPS2024 paper, DiffusionPDE, leverages the power of diffusion models to tackle this challenge!
Solving PDEs with limited data and system observations is a critical yet often overlooked challenge. Our #NeurIPS2024 paper, DiffusionPDE, leverages the power of diffusion models to tackle this challenge!
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