@conference {cCaselles, title = {SIRA: Relightable Avatars from a Single Image}, booktitle = {Winter Conference on Applications of Computer Vision (WACV)}, year = {2023}, abstract = {

Recovering the geometry of a human head from a single image, while factorizing the materials and illumination is a severely ill-posed problem that requires prior information to be solved. Methods based on 3D Morphable Models (3DMM), and their combination with differentiable renderers, have shown promising results. However, the expressiveness of 3DMMs is limited, and they typically yield over-smoothed and identity-agnostic 3D shapes limited to the face region. Highly accurate full head reconstructions have recently been obtained with neural fields that parameterize the geometry using multilayer perceptrons. The versatility of these representations has also proved effective for disentangling geometry, materials and lighting. However, these methods require several tens of input images. In this paper, we introduce SIRA, a method which, from a single image, reconstructs human head avatars with high fidelity geometry and factorized lights and surface materials. Our key ingredients are two data-driven statistical models based on neural fields that resolve the ambiguities of single-view 3D surface reconstruction and appearance factorization. Experiments show that SIRA obtains state of the art results in 3D head reconstruction while at the same time it successfully disentangles the global illumination, and the diffuse and specular albedos. Furthermore, our reconstructions are amenable to physically-based appearance editing and head model relighting.

}, author = {Caselles, Pol and Ramon, Eduard and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto and Moreno, Francesc and Triginer, Gil} } @mastersthesis {xCaselles21, title = {Disentangling neural network structure from the weights space}, year = {2021}, abstract = {

Deep Neural Networks have been used to tackle a wide variety of tasks achieving great performance. However, there is still a lack of knowledge of how the training of these models converge and how weights relate to their properties. In this thesis we investigate the structure of the weight space and try to disentangle its properties. Attention mechanisms are introduced to capture relations among neurons{\textquoteright} weights that help in weight reconstruction, hyper-parameter classification and accuracy prediction. Our approach further has the potential to work with variable input size allowing different network width, depth or even architecture types.

}, author = {Caselles, Pol}, editor = {Sch{\"u}rholt, Konstantin and Borth, Damian and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCaselles, title = {Integrating low-level motion cues in deep video saliency}, year = {2019}, abstract = {

This thesis investigates the importance of motion when predicting saliency in videos. Naturally, humans observe both dynamic and static objects. When we are focused on watching a video, we tend to keep our eyes on the objects that are moving in the scene, items that we quickly recognize, as well as to those that attract our attention. In this work, different experiments are presented to corroborate this implication. Various approaches will be shown implementing an adaptation of the SalBCE neural network by using only motion. A simple implementation is proposed for the generation of saliency maps using previously extracted static and dynamic information from the images. The DHF1K dataset has been used for the experiment{\textquoteright}s realization.

}, keywords = {Motion, Saliency, video}, author = {Caselles, Pol}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} }