5 citations · 6 across the 19 of their papers we have counts for
24 papers
Telescopic Language Models
Zhilin Guo, Boqiao Zhang, Hakan Aktas +14
One deployed language model must often serve many compute budgets, yet serving each budget still means a separate training or compression run per point. We train a Telescopic Langu…
Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose
Zhilin Guo, Boqiao Zhang, Oszkár Urbán +8
Sparse inertial pose estimation promises camera-free motion capture from consumer devices, but consumer sensors are unreliable: firmware-fused orientations are biased, mounting var…
One Sensor, Whole Body - 3D Body Pose from a Single Consumer Earbud IMU
Zhilin Guo, Boqiao Zhang, Oszkár Urbán +8
Consumer earbuds already stream inertial motion data from the head, one of the most widely worn sensor locations on the body. We ask how much of the 3D body pose a single such head…
ResEdit: Residual embeddings for precise generative image editing
Ahmet Canberk Baykal, Valentin Deschaintre, Yannick Hold-Geoffroy +4
Conditional diffusion image generators can be repurposed for editing through inversion, without the need for large-scale paired fine-tuning data. However, producing high-quality, t…
Matryoshka Gaussian Splatting
Zhilin Guo, Boqiao Zhang, Hakan Aktas +11
The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existi…
Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity
Zheyuan Hu, Weitao Chen, Cengiz Öztireli +2
Partial Differential Equations are precise in modelling the physical, biological and graphical phenomena. However, the numerical methods suffer from the curse of dimensionality, hi…
PoseCraft: Tokenized 3D Body Landmark and Camera Conditioning for Photorealistic Human Image Synthesis
Zhilin Guo, Jing Yang, Kyle Fogarty +9
Digitizing humans and synthesizing photorealistic avatars with explicit 3D pose and camera controls are central to VR, telepresence, and entertainment. Existing skinning-based work…
FreNBRDF: A Frequency-Rectified Neural Material Representation
Chenliang Zhou, Zheyuan Hu, Cengiz Oztireli
Accurate material modeling is crucial for achieving photorealistic rendering, bridging the gap between computer-generated imagery and real-world photographs. While traditional appr…
Restereo: Diffusion stereo video generation and restoration
Xingchang Huang, Ashish Kumar Singh, Florian Dubost +6
Stereo video generation has been gaining increasing attention with recent advancements in video diffusion models. However, most existing methods focus on generating 3D stereoscopic…
RETRO: REthinking Tactile Representation Learning with Material PriOrs
Weihao Xia, Chenliang Zhou, Cengiz Oztireli
Tactile perception is profoundly influenced by the surface properties of objects in contact. However, despite their crucial role in shaping tactile experiences, these material char…
CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts
Chong Su, Yingbin Fu, Zheyuan Hu +6
We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically structured indoor digital twins…
Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Hanxue Liang, Jiawei Ren, Ashkan Mirzaei +8
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle wi…
Physically Based Neural Bidirectional Reflectance Distribution Function
Chenliang Zhou, Alejandro Sztrajman, Gilles Rainer +6
We introduce the physically based neural bidirectional reflectance distribution function (PBNBRDF), a novel, continuous representation for material appearance based on neural field…
Blue noise for diffusion models
Xingchang Huang, Corentin Salaün, Cristina Vasconcelos +3
Most of the existing diffusion models use Gaussian noise for training and sampling across all time steps, which may not optimally account for the frequency contents reconstructed b…
An Information Theoretic Approach to Machine Unlearning
Jack Foster, Kyle Fogarty, Stefan Schoepf +3
To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in u…
Differentiable Visual Computing for Inverse Problems and Machine Learning
Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas +4
Originally designed for applications in computer graphics, visual computing (VC) methods synthesize information about physical and virtual worlds, using prescribed algorithms optim…
Hypernetworks for Generalizable BRDF Representation
Fazilet Gokbudak, Alejandro Sztrajman, Chenliang Zhou +3
In this paper, we introduce a technique to estimate measured BRDFs from a sparse set of samples. Our approach offers accurate BRDF reconstructions that are generalizable to new mat…
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation
Chenliang Zhou, Fangcheng Zhong, Param Hanji +5
We propose FrePolad: frequency-rectified point latent diffusion, a point cloud generation pipeline integrating a variational autoencoder (VAE) with a denoising diffusion probabilis…
Neural Fields with Hard Constraints of Arbitrary Differential Order
Fangcheng Zhong, Kyle Fogarty, Param Hanji +6
While deep learning techniques have become extremely popular for solving a broad range of optimization problems, methods to enforce hard constraints during optimization, particular…
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
Hanxue Liang, Tianhao Wu, Param Hanji +4
Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured i…
Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity
Tianhao Wu, Hanxue Liang, Fangcheng Zhong +4
Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimizat…
3D GAN Inversion with Facial Symmetry Prior
Fei Yin, Yong Zhang, Xuan Wang +8
Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion m…
One-shot Detail Retouching with Patch Space Neural Transformation Blending
Fazilet Gokbudak, Cengiz Oztireli
Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality ret…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…