Got a paper around appearance at CVPR 2026? Make the most of it at the AppX workshop! Submit by April 7: appx-workshop.github.io @ele_g2 @vdeschaintre @michi_fischer @giuvecchio95 #CVPR2026 #ComputerVision #NeuralRendering #AppearanceUnderstanding #AppX2026

I was at #AdobeMAX this week to present #projectSurfaceSwap! Our surface selection and replacement techs! Check it out here: youtu.be/Xg4n60hYfhA?si=b1Zo… What a blast the Sneaks were!

Adobe Research is heading to #SIGGRAPH2025, the premiere conference for computer graphics and interactive techniques! From generative image editing to 3D reconstruction, we’re sharing 25 papers this year – this teaser is just a glimpse. 🔗 adobe.ly/3J5WGTm Come find us at @siggraph and explore the future of creativity!

I didn't even expect it will work on anime images! Check this out!

Cool! Try the rgbx wrapper in ComfyUI made by @toyxyz3. If you're interested about the technical details, check out our paper at zheng95z.github.io/publicati….

I created the ComfyUI_rgbx_Wrapper node. Try using rgbx in ComfyUI! #AIイラスト #AI #Comfyui

🎓 We introduce SAMa! A material selection and segmentation model on 3D models in any format (3DGS, NeRF, Mesh). Given a user click, we propose to select all regions on an objects with the same material. We can also do segmentation in under a minute: mfischer-ucl.github.io/sama/

We’re thrilled to release the implementation and model weights for our SIGGRAPH 2024 paper, "RGB↔X: Image Decomposition and Synthesis Using Material- and Lighting-aware Diffusion Models"! 📂 GitHub Repository: github.com/zheng95z/rgbx RGB↔X is a unified diffusion-based framework that enables realistic image analysis (intrinsic channel estimation, referred to as RGB→X) and synthesis (realistic rendering from intrinsic channels, referred to as X→RGB). This framework explores the connections between diffusion models, realistic rendering, and intrinsic decomposition. We believe this can benefit a wide range of downstream tasks, including material editing, relighting, and realistic rendering from simple or under-specified scene definitions. TL;DR for graphics people: You can now use diffusion models to eliminate or simulate global illumination in image-space on G-buffers. 🌐 Project Page: zheng95z.github.io/publicati…

We present a learning-based rendering method that uses normalizing flows to efficiently importance sample product distributions comprising distant emitters (i.e., HDRI lighting). This is joint work with my amazing collaborators @MilosHasan, @fujun_luan, @krishnamullia, and @iliyang at Adobe Research. 1/5

What a pleasant big surprise. Free to share a $1M secret to get a best paper award: to get Iliyan presenting the paper. @EGSympRendering @iliyang. Congrats to @tzumaoli, Trevor and Ravi.

No more huge output PDFs in Overleaf. Put this file in your project root and it will compress non-JPEG images. Default quality settings are high, works even for noisy images. gist.github.com/iliyang/b087…

Check out Adobe Research's new work in Computer Vision at #CVPR2024! research.adobe.com/publicati…

Our work on #NBVH (Neural ray queries with Bounding Volume Hierarchies) will be presented this summer at #SIGGRAPH2024! What's that? See🧵below ⤵️ 📃Source Code: github.com/WeiPhil/nbvh 🌐Project Page/Paper: weiphil.github.io/portfolio/… (ft. @PhilippeWeier @iliyang @boubek et al.)

Ever wanted to load a large asset in your GPU path tracer, only to find it doesn't fit in memory? Then check out our #SIGGRAPH2024 paper “N-BVH: Neural ray queries with bounding volume hierarchies” (with Alexander Rath, @exppad, @iliyang, Philipp Slusallek and @boubek)

📸You took a room photo or generated one with your favourite Diffusion model? Want to turn it into 3D but find finding and placing the assets is boring? 📚Checkout PSDR-Room for turning a single photo to scene using differentiable rendering & retrieval! shuangz.com/projects/psdr-ro…

We continued our journey on the path (tracing) of tessellation-free displacement with RMIP, combining inverse displacement with rectangular mipmapping, yielding up to an order of magnitude faster execution. See our SIGGRAPH Asia 2023 paper for details: research.adobe.com/publicati…