Prof. of Computer Science at KAUST | Computer Vision, Machine Learning, Computer Graphics, Deep Learning
Thuwal, Saudi Arabia
Joined November 2021
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Peter Wonka retweeted
Happy to share that Helix4D is @NeurIPSConf oral
Big thank you to all of my advisors and collaborators who made this possible!
See you at NeurIPS 🧬
🧬 [1/3] Videos → dynamic 3D meshes 🧬
Excited to share Helix4D, our framework for complex 4D mesh generation from video!
It handles hard 4D cases: topology changes, inner surfaces, transparency, deformation, and thin structures.
👉 Project: snap-research.github.io/heli…
Peter Wonka retweeted
If you are at ECCV next week, drop by our poster AHOY! Animatable 3D humans from occluded monocular YouTube video
We will be presenting twice.
📍 3D Humans Workshop, 8 Sept, 1200--1500
📍 Main conf, 11 Sept, ExHall #236 1030-1200
miraymen.github.io/ahoy/
Peter Wonka retweeted
1/4 📢 AHOY! accepted at #ECCV2026! 🎉
From a single in-the-wild YouTube video, often heavily occluded, AHOY builds a complete, animatable 3D human avatar.
Each clip below runs input → 360° avatar → animation. 🧵👇
🔗 miraymen.github.io/ahoy/
How much 3D do you actually see in a photo? 👀
A depth map stops at the visible surfaces. Our #ICML2026 paper LaRI (ruili3.github.io/lari) goes further: predicting the hidden geometry with layered point maps, in one feed-forward. (1/3)
Peter Wonka retweeted
🧬 [1/3] Videos → dynamic 3D meshes 🧬
Excited to share Helix4D, our framework for complex 4D mesh generation from video!
It handles hard 4D cases: topology changes, inner surfaces, transparency, deformation, and thin structures.
👉 Project: snap-research.github.io/heli…
Peter Wonka retweeted
Excited to share that EasyV2V has been accepted to CVPR 2026! Data is the fuel. EasyV2V introduces a comprehensive and scalable video editing data pipeline. Curious how different types of data impact video editing performance? Check out our project page:
snap-research.github.io/easy…
🧵1/N
🔥Introducing EasyV2V from Snap Research!
📌TLDR: We propose a simple and powerful model for instructional video editing!
Just use a text prompt to edit your video!
#Aiart #AIArtwork #Prompt #ArtificialIntelligence
#GenerativeAI #MachineLearning #AI
Peter Wonka retweeted
Meet our instructional video editing model, EasyV2V. Think nano-banana for videos. 🍌🎬
Turn simple instructions into powerful video edits.
Watch it in action 👉 snap-research.github.io/easy…
🧵1/N
🔥Introducing EasyV2V from Snap Research!
📌TLDR: We propose a simple and powerful model for instructional video editing!
Just use a text prompt to edit your video!
#Aiart #AIArtwork #Prompt #ArtificialIntelligence
#GenerativeAI #MachineLearning #AI
Peter Wonka retweeted
KAUST is hiring!
The Computer Science Program at #KAUST is inviting applications for faculty positions in Artificial Intelligence, with a focus on generative AI, #LLMs, or agentic AI, and in Large Scale High-Performance Computing.
With national investments driving massive growth in #AI infrastructure, data centers, and supercomputing, Saudi Arabia is creating one of the most advanced AI ecosystems in the world, and KAUST is driving this transformation through cutting-edge research, pioneering innovation, and cross-sector partnerships that connect discovery to real-world impact.
We welcome applications from across the spectrum of computer science, with current priorities in these fields.
Learn more and apply here:apply.interfolio.com/176558
#AgenticAI #HPC #FacultyPositions
Peter Wonka retweeted
📢V2M4 (Accepted by ICCV 2025)
We reconstruct 4D mesh animations from a single video with consistency in pose, appearance, topology, and texture.
project: windvchen.github.io/V2M4/
arxiv: arxiv.org/abs/2503.09631
Collaboration with @_BiaoZhang , Xiangjun Tang and @peter_wonka
Peter Wonka retweeted
[1/9] 🚀 We introduce 4Real-Video-V2, a method that can generate 4D scenes from a simple text prompt, viewable from any angle at any moment in time. It’s fast, photorealistic, and works on full scenes. Here's how it works and why it matters. 👇
snap-research.github.io/4Rea…
Peter Wonka retweeted
Our paper, "Training-free Video Semantic Segmentation based on Diffusion Models," was accepted at CVPR 2025!
A great collaboration between KAUST and MPI.
Image diffusion models are powerful for image generation and serve as strong backbones with rich semantic understanding.
Peter Wonka retweeted
Update about the 4th Monocular Depth Estimation Workshop at #CVPR2025:
🎉 Website is LIVE! (link below)
🎉 Keynotes: Peter Wonka (@peter_wonka), Yiyi Liao (@yiyi_liao_), and Konrad Schindler
🎉 Challenge updates: new prediction types, baselines & metrics
Peter Wonka retweeted
Our preprint on deep learning based high-throughout human #blastoid classification is out It helps unbiased quality assessment of a large number of blastoids thx for Prof @peter_wonka and his team’s collaboration @KAUST_BESE @KaustResearch @biorxivpreprint biorxiv.org/cgi/content/shor…
New papers posted on December 8, 2024:
2 in developmental biology: biorxiv.org/collection/devel…
1 in evolutionary biology: biorxiv.org/collection/evolu…
2 in genetics: biorxiv.org/collection/genet…
2 in genomics: biorxiv.org/collection/genom…
1 in immunology: biorxiv.org/collection/immun…
Peter Wonka retweeted
📢📢 𝐏𝐫𝐄𝐝𝐢𝐭𝐨𝐫𝟑𝐃: 𝐅𝐚𝐬𝐭 𝐚𝐧𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐞 𝟑𝐃 𝐒𝐡𝐚𝐩𝐞 𝐄𝐝𝐢𝐭𝐢𝐧𝐠 📢📢
We propose a training-free 3D shape editing approach that rapidly and precisely edits the regions intended by the user and keeps the rest as is.
Using a quickly brushed mask and a text prompt, we first apply multi-view editing in the 2D domain and then run our merging algorithm in the 3D feature space to ensure that the edited shape is loyal to the input shape.
Project Page: ziyaerkoc.com/preditor3d/
Video: youtube.com/watch?v=Ty2xXaEu…
Great work by @ErkocZiya @cangumeli Chaoyang Wang @angelaqdai @peter_wonka @hyjameslee @PeiyeZ
Peter Wonka retweeted
T-3DGS: Removing Transient Objects for 3D Scene Reconstruction
Vadim Pryadilshchikov, Alexander Markin, Artem Komarichev, @rusrakhimov, @peter_wonka, @burnaevevgeny
tl;dr: unsupervised transient detector and transient mask propagation
arxiv.org/abs/2412.00155
Peter Wonka retweeted
📢𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐲 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬
We propose to represent general geometries as distributions using diffusion models.
project site: 1zb.github.io/GeomDist/
arxiv: arxiv.org/abs/2411.16076
dataset: huggingface.co/datasets/Zbal…
Collaboration with Jing Ren and @peter_wonka
Peter Wonka retweeted
Happy to share that our Vivid-Zoo paper has been accepted at #NeurIPS2024. The paper is collaborated with
@1zhengcheng
@WenxuanZhu75798
@_BiaoZhang
@peter_wonka
@BernardSGhanem
🚀Paper accepted at #NeurIPS2024!
⭐️Vivid-ZOO: Multi-View Video Generation with Diffusion Model
Authors: Bing Li, Cheng Zheng, Wenxuan Zhu, Jinjie Mai, Biao Zhang, Peter Wonka, @BernardSGhanem
📰ArXiv: arxiv.org/pdf/2406.08659
Peter Wonka retweeted
Join the #KAUST Global Fellowship Program, an incredible opportunity for exceptional postdocs with groundbreaking ideas to advance Saudi's research priorities. The program offers access to cutting-edge research tools, top-notch facilities and expert mentorship.
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
Hanan Gani (@hanan_shafi29), Shariq Farooq Bhat (@shariq_farooq), Muzammal Naseer, Salman Khan, Peter Wonka
ICLR 2024
hananshafi.github.io/llm-blu…