Eric Chan @ericryanchan
PhD Student, Stanford University ericryanchan.github.io Joined December 2020-
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This paper has received significantly less attention than it deserves, so let me shed a bit more light on it and describe why it's so good: 1. It turns out that the classical U-Net image diffusion backbone, which the entire community has been happily building upon during the…
This paper has received significantly less attention than it deserves, so let me shed a bit more light on it and describe why it's so good: 1. It turns out that the classical U-Net image diffusion backbone, which the entire community has been happily building upon during the…
📢📢📢 thrilled to announce "𝟒𝐃-𝐟𝐲: 𝐓𝐞𝐱𝐭-𝐭𝐨-𝟒𝐃 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐇𝐲𝐛𝐫𝐢𝐝 𝐒𝐜𝐨𝐫𝐞 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠" sherwinbahmani.github.io/4dfy Way to start @sherwinbahmani 🎉 PhD co-advised with @DaveLindell at #UofT.
I’m really excited to finally share our new paper “ZeroNVS: Zero-shot 360-degree View Synthesis from a Single Real Image.” The paper, webpage, and code are all released! 📖arxiv.org/abs/2310.17994 🌐kylesargent.github.io/zeronvs/ 🖥️github.com/kylesargent/Ze… 🧵is below.
Diffusion models are awesome! Check out our survey on 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐟𝐨𝐫 𝐕𝐢𝐬𝐮𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠! We give an introduction to diffusion models and highlight how they are used by state-of-the-art methods in graphics and vision. arxiv.org/abs/2310.07204
Eric Chan @ericryanchan will be presenting our #ICCV2023 work GeNVS at the "Generative AI 1" Oral session today (Oct. 4th 04:30-06:00 PM)! If you're interested in this work or want to chat with us, come visit our poster 190 in Room "Foyer Sud."
Eric Chan @ericryanchan will be presenting our #ICCV2023 work GeNVS at the "Generative AI 1" Oral session today (Oct. 4th 04:30-06:00 PM)! If you're interested in this work or want to chat with us, come visit our poster 190 in Room "Foyer Sud."
Given a single image, have you ever dreamed about how it would look like if you fly into it? We present DiffDreamer for 3D-consistent long-range scene extrapolation! #ICCV2023 Come to chat with @prime_cai, @ericryanchan and me at our poster 10:30 am today in Foyer Sud!
Given a single image, have you ever dreamed about how it would look like if you fly into it? We present DiffDreamer for 3D-consistent long-range scene extrapolation! #ICCV2023 Come to chat with @prime_cai, @ericryanchan and me at our poster 10:30 am today in Foyer Sud! https://t.co/5IOfThWY7l
Introducing “FlowCam: Training Generalizable 3D Radiance Fields w/o Camera Poses via Pixel-Aligned Scene Flow”! We train a generalizable 3D scene representation self-supervised on datasets of raw videos, without any pre-computed camera poses or SFM! cameronosmith.github.io/flowcam 1/n
We also present another paper at @siggraph 2023 on neural implicit 3D Morphable Models that can be used to create a dynamic 3D avatar from a single in-the-wild image. (Lead author @connorzl). research.nvidia.com/labs/toronto-a…
AI-driven 3D lifting has huge potential! Choose your favorite AI tool to generate/customize an avatar and bring it to life in 3D – something not possible with 3D scanning-based systems. If you come to @siggraph, try a live demo as part of Emerging Technologies #SIGGRAPH2023
AI-driven 3D lifting has huge potential! Choose your favorite AI tool to generate/customize an avatar and bring it to life in 3D – something not possible with 3D scanning-based systems. If you come to @siggraph, try a live demo as part of Emerging Technologies #SIGGRAPH2023 https://t.co/0ajdP7etBj
We're organizing an #ICLR2023 workshop titled: 🌟Neural Fields across Fields: Methods and Applications of Implicit Neural Representations🌟 sites.google.com/view/neural-fi… Please submit your cool research (due: 3rd Feb)! Looking forward to seeing you on 05/05/23 @ Kigali, Rwanda 🇷🇼
We are excited to present our work at #NeurIPS22 on Generative Neural Articulated Radiance Fields! In this paper, we build on top of the capabilities of existing 3D GAN architectures and propose the first method for generating full human bodies! (1/7)
Excited to share our #NeurIPS2022 @NVIDIAAI work GET3D, a generative model that directly produces explicit textured 3D meshes with complex topology, rich geometric details, and high fidelity textures. #3D Project page: nv-tlabs.github.io/GET3D/
also if you come to our poster early, we (@ericryanchan, Matthew, @shalinidemello ) will show a live 3D visualizer demo running on a laptop for the first 30 mins or so as long as the battery lasts
If you are at #CVPR2022, please come see "EG3D" Friday morning 𝐓𝐚𝐥𝐤: 𝟗:𝟑𝟎𝐚𝐦~ @ 𝐆𝐫𝐞𝐚𝐭 𝐇𝐚𝐥𝐥 𝐀-𝐃 𝐏𝐨𝐬𝐭𝐞𝐫𝐬 (𝟏𝟑𝐚) 𝟏𝟎:𝟎𝟎-𝟏𝟐:𝟑𝟎𝐩𝐦 @ 𝐇𝐚𝐥𝐥𝐬 𝐁𝟐-𝐂 Code & models released: github.com/NVlabs/eg3d joint work w @NVIDIAAI and @GordonWetzstein
Introducing "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering"! vsitzmann.github.io/lfns (w/ video!) LFNs are the first fully implicit neural scene representation with real-time rendering, without post-processing / hybrid data-structures! (1/n)
Our new work on scaling up neural representations is out! Adaptive coordinate networks (ACORN) can be trained to represent high-res imagery (10s of MP) in seconds & scale up to gigapixel images and highly detailed 3D models. computationalimaging.org/publications/a… arxiv.org/abs/2105.02788 1/n
Happy π day! How many pies would @ericryanchan and I have to eat to get enough images to train pi-GAN on pies? marcoamonteiro.github.io/pi-GAN-website/ #CVPR #GAN #AI
Our NeurIPS 2020 paper "MetaSDF" shows how to rapidly fit neural implicit representations with gradient-based meta-learning. We wrote a Colab with a stand-alone implementation of MAML, Siren, and ReLU MLPs so you can jump right in and easily extend it! colab.research.google.com/github/vsitzma…
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This paper has received significantly less attention than it deserves, so let me shed a bit more light on it and describe why it's so good: 1. It turns out that the classical U-Net image diffusion backbone, which the entire community has been happily building upon during the…
Analyzing and Improving the Training Dynamics of Diffusion Models paper page: huggingface.co/papers/2312.02… Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper, we identify and rectify several…
Diffusion models are awesome! Check out our survey on 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐟𝐨𝐫 𝐕𝐢𝐬𝐮𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠! We give an introduction to diffusion models and highlight how they are used by state-of-the-art methods in graphics and vision. arxiv.org/abs/2310.07204
Eric Chan @ericryanchan will be presenting our #ICCV2023 work GeNVS at the "Generative AI 1" Oral session today (Oct. 4th 04:30-06:00 PM)! If you're interested in this work or want to chat with us, come visit our poster 190 in Room "Foyer Sud."
Generative Novel View Synthesis with 3D-Aware Diffusion Models abs: arxiv.org/abs/2304.02602 project page: nvlabs.github.io/genvs/
Given a single image, have you ever dreamed about how it would look like if you fly into it? We present DiffDreamer for 3D-consistent long-range scene extrapolation! #ICCV2023 Come to chat with @prime_cai, @ericryanchan and me at our poster 10:30 am today in Foyer Sud!
Freshly accepted to #ICCV2023! Trained on **images only**, we can freely extrapolate into or out of a scene. Joint effort with an amazing team @ericryanchan @songyoupeng @MShahbazi_ @AntonObukhov1 Luc Van Gool @GordonWetzstein primecai.github.io/diffdreamer
Ok, looks like I found the best way to describe NeRF in the related work section
We are excited to present our work at #NeurIPS22 on Generative Neural Articulated Radiance Fields! In this paper, we build on top of the capabilities of existing 3D GAN architectures and propose the first method for generating full human bodies! (1/7)
PhD students presenting their findings to their supervisor:
GAUDI: A Neural Architect for Immersive 3D Scene Generation abs: arxiv.org/abs/2207.13751 github: github.com/apple/ml-gaudi
This clip by Dust-to-Digital demonstrates you can record sounds with some cups. When the kid's screams into the top paper cup, a little needle is etching a pattern into the glass. Then the groove and the needle play the sounds back [source, read more: buff.ly/3oqSl0K]
Generative Neural Articulated Radiance Fields abs: arxiv.org/abs/2206.14314 project page: computationalimaging.org/publications/g…
also if you come to our poster early, we (@ericryanchan, Matthew, @shalinidemello ) will show a live 3D visualizer demo running on a laptop for the first 30 mins or so as long as the battery lasts
If you are #CVPR2022 in person, check out EG3D. We learn to generate depth images from single images via tri-plane representations — without depth or mutiview supervision. Oral on Friday 6/24 9:30am, and poster and live demo at board 13a at 10:00am. Code: github.com/NVlabs/eg3d
If you are at #CVPR2022, please come see "EG3D" Friday morning 𝐓𝐚𝐥𝐤: 𝟗:𝟑𝟎𝐚𝐦~ @ 𝐆𝐫𝐞𝐚𝐭 𝐇𝐚𝐥𝐥 𝐀-𝐃 𝐏𝐨𝐬𝐭𝐞𝐫𝐬 (𝟏𝟑𝐚) 𝟏𝟎:𝟎𝟎-𝟏𝟐:𝟑𝟎𝐩𝐦 @ 𝐇𝐚𝐥𝐥𝐬 𝐁𝟐-𝐂 Code & models released: github.com/NVlabs/eg3d joint work w @NVIDIAAI and @GordonWetzstein
If you are at #CVPR2022, please come see "EG3D" Friday morning 𝐓𝐚𝐥𝐤: 𝟗:𝟑𝟎𝐚𝐦~ @ 𝐆𝐫𝐞𝐚𝐭 𝐇𝐚𝐥𝐥 𝐀-𝐃 𝐏𝐨𝐬𝐭𝐞𝐫𝐬 (𝟏𝟑𝐚) 𝟏𝟎:𝟎𝟎-𝟏𝟐:𝟑𝟎𝐩𝐦 @ 𝐇𝐚𝐥𝐥𝐬 𝐁𝟐-𝐂 Code & models released: github.com/NVlabs/eg3d joint work w @NVIDIAAI and @GordonWetzstein
According to AMiner, I'm the 5th most influential scholar in computer graphics. I'm honored and truly thankful to my amazing students, postdocs, and collaborators! aminer.cn/ai2000/cg
Introducing "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering"! vsitzmann.github.io/lfns (w/ video!) LFNs are the first fully implicit neural scene representation with real-time rendering, without post-processing / hybrid data-structures! (1/n)
In personal news, I’m thrilled to announce that I’ll be joining @MIT as tenure-track assistant professor in July 2022! My lab will investigate neural scene representations, inverse graphics, neural rendering, and their applications in vision, graphics, robotics, and AI! (1/n)
Thrilled to announce that I'll be joining @UofTCompSci as a tenure-track assistant professor starting July 2022! I'm grateful to all those who have supported me thus far, and am excited to work with amazing students and colleagues in Toronto! (1/n)
Lab meeting when you don’t have new results
The dream of computer vision as inverse graphics in on track to become real / dominant approach as computer graphics continues to rewrite its rendering stack with differentiable components (here: quad/octrees). ConvNets should output NeRFs. Very exciting!
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