Name: | Peter Kocsis |
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Position: | Ph.D Candidate |
E-Mail: | peter.kocsis@tum.de |
Phone: | +49 (89) 289 - 18164 |
Room No: | 02.07.038 |
Intrinsic Image Diffusion for Single-view Material Estimation |
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Peter Kocsis, Vincent Sitzmann, Matthias Nießner |
CVPR 2024 |
Appearance decomposition is an ambiguous task and collecting real data is challenging. We utilize a pre-trained diffusion model and formulate the problem probabilistically. We fine-tune a pre-trained diffusion model conditioned on a single input image to adapt its image prior to the prediction of albedo, roughness and metallic maps. With our sharp material predictions, we optimize for spatially-varying lighting to enable photo-realistic material editing and relighting. |
[video][bibtex][project page] |
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes |
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Peter Kocsis, Peter Sukenik, Guillem Braso, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi |
NeurIPS 2022 |
We show that adding fully-connected layers is beneficial for the generalization of convolutional networks in the tasks working in the low-data regime. Furthermore, we present a novel online joint knowledge distillation method (OJKD), which allows us to utilize additional final fully-connected layers during training but drop them during inference without a noticeable loss in performance. Doing so, we keep the same number of weights during test time. |
[code][bibtex][project page] |