Giuseppe Vecchio

AI researcher into generative models and graphics. 📸 Passionate about photography and travel. Hiking enthusiast. Balancing tech with creativity!

[Iliyan Georgiev @iliyang]

Got a paper around appearance at CVPR 2026? Make the most of it at the AppX workshop! Submit by April 7:
https://t.co/ZFZp34mLY7

@ele_g2 @vdeschaintre @michi_fischer
@giuvecchio95

#CVPR2026 #ComputerVision #NeuralRendering #AppearanceUnderstanding #AppX2026

[Giuseppe Vecchio @giuvecchio95]

Happy to share that StableMaterials has been accepted at #CVPR2026.
Read more: https://t.co/57zADeexse
See you in Denver.

[#CVPR2026 @CVPR]

#CVPR2025 Area Chairs (ACs) identified a number of highly irresponsible reviewers, those who either abandoned the review process entirely or submitted egregiously low-quality reviews, including some generated by large language models (LLMs).

1/2

[Giuseppe Vecchio @giuvecchio95]

@exppad @vdeschaintre Congrats 🎉👏

[Tamy Boubekeur @boubek]

ControlMat casts "image-to-material" as a controlled generative task, guiding in-distribution channels diffusion with the input and introducing diffusion components such "Noise Rolling" for tileability. More in our ToG paper (presented Friday at SIG Asia): https://t.co/TuIKuPbpfM

[Tamy Boubekeur @boubek]

Adobe Research France is hiring interns for 2025. If you are pursuing a (research oriented) M. Sc. or a Ph. D. in 3D graphics or vision, consider applying :
Paris: https://t.co/VO2h9Z919D
Lyon or Clermont-Ferrand: https://t.co/qnLiM4Y773

[Valentin Deschaintre @vdeschaintre]

Tired of working with textures? Want to enable relighting with materials but you don't know where to find data?

Come to our poster at CVPR! 4F Arch Building, poster 246.

[Renato Sortino @Rensortino]

Happy to have been a part of @CVPR presenting “MatFuse: Controllable Material Generation with Diffusion Models” with my colleague @giuvecchio95 .

Project Page: https://t.co/mYNgh9ZrHb

#CVPR2024

[Giuseppe Vecchio @giuvecchio95]

🚀Excited to present at @CVPR our paper introducing #MatSynth, a new large scale PBR materials dataset with permissive licensing!

📅Join us at the poster session on Fri 21st at 10:30 AM.

🌐Project page: https://t.co/lqkiFCnIGB
🤗Release: https://t.co/n9AJ1e24EF

[Giuseppe Vecchio @giuvecchio95]

📢Join us next week @CVPR, together with @Rensortino we present #MatFuse a novel method for material synthesis and editing using diffusion models.

We will be at the poster session on Wed 19 at 10:30.

🌐Project page: https://t.co/oaYoMk8HyR
💻Source: https://t.co/AxXV7ZNdQk

[Walter Scheirer @wjscheirer]

The Computer Vision Foundation open access proceedings team is proud to announce that the #CVPR2024 proceedings is now online:

Main conference: https://t.co/ZCVXRO8NwR

Workshops: https://t.co/RyPgGlKIfd

Enjoy and I'll see all of you in Seattle!

[Giuseppe Vecchio @giuvecchio95]

MatFuse also allows users to have map-level material editing capabilities through latent manipulation. We learn a disentangled latent representation for each map, allowing us to manipulate the specific parts of the latent space corresponding material property we want to edit.

[Giuseppe Vecchio @giuvecchio95]

🎉 Exciting news! Our paper titled “MatFuse: Controllable Material Generation with Diffusion Models” has been accepted at #CVPR2024!

MatFuse introduces a novel approach to material synthesis and editing, leveraging diffusion models.

More at: https://t.co/d98GMqzwHw

[Giuseppe Vecchio @giuvecchio95]

MatFuse gives a fine-grained control over the over material synthesis combining multiple sources of conditioning, including color palettes, sketches, text, and pictures.

[Valentin Deschaintre @vdeschaintre]

📢 Permissively licensed SVBRDF Dataset!
https://t.co/ct11LQXpAG
📚SVBRDF Ground truth Material Data with permissive license has been challenging to get or gather in the last few years.

We present MatSynth, with 4000+ carefully curated materials with permissive licences

[Ben Ferns @ben_ferns]

ControlMat: Controlled Generative Approach to Material Capture by Giuseppe Vecchio et al. From a single photo to clean, realistics, tileable PBR materials with this diffusion-based matching model.

https://t.co/YNYaoIYwKJ

[AK @_akhaliq]

ControlMat: A Controlled Generative Approach to Material Capture

paper page: https://t.co/pDSHly04BO

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space-optimization methods, and carefully validate our diffusion process design choices.

[Tamy Boubekeur @boubek]

Our new Image-to-Material (AI Powered) technology just shipped in Adobe Substance 3D Sampler.

[Valentin Deschaintre @vdeschaintre]

📚We will present our paper "The visual Language of Fabric" at Siggraph in a couple of weeks with @juliagviu!
🌟We explore how people describe materials (fabrics) and perceive them: what concepts and lemmas are important, and L-V models sensitivities.

https://t.co/ymRpXPM14E