Zobrazeno 1 - 10
of 595
pro vyhledávání: '"de la Torre, Fernando"'
Autor:
Kwon, Youngjoong, Fang, Baole, Lu, Yixing, Dong, Haoye, Zhang, Cheng, Carrasco, Francisco Vicente, Mosella-Montoro, Albert, Xu, Jianjin, Takagi, Shingo, Kim, Daeil, Prakash, Aayush, De la Torre, Fernando
Recent progress in neural rendering has brought forth pioneering methods, such as NeRF and Gaussian Splatting, which revolutionize view rendering across various domains like AR/VR, gaming, and content creation. While these methods excel at interpolat
Externí odkaz:
http://arxiv.org/abs/2407.12777
3D Hand reconstruction from a single RGB image is challenging due to the articulated motion, self-occlusion, and interaction with objects. Existing SOTA methods employ attention-based transformers to learn the 3D hand pose and shape, but they fail to
Externí odkaz:
http://arxiv.org/abs/2407.09646
Autor:
Mallick, Saswat Subhajyoti, Goel, Rahul, Kerbl, Bernhard, Carrasco, Francisco Vicente, Steinberger, Markus, De La Torre, Fernando
3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with its fast, interpretable, and high-fidelity rendering. However, its resource requirements limit its usability. Especially on constrained devices, training performance degrades quic
Externí odkaz:
http://arxiv.org/abs/2406.15643
Autor:
Milacski, Zoltán Á., Niinuma, Koichiro, Kawamura, Ryosuke, de la Torre, Fernando, Jeni, László A.
The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for one problem. The complexity introduced by multiple modalities makes cap
Externí odkaz:
http://arxiv.org/abs/2405.18438
Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In
Externí odkaz:
http://arxiv.org/abs/2402.14792
Autor:
Wu, Chen, De la Torre, Fernando
Text-to-image diffusion models have achieved remarkable performance in image synthesis, while the text interface does not always provide fine-grained control over certain image factors. For instance, changing a single token in the text can have unint
Externí odkaz:
http://arxiv.org/abs/2402.13490
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques. Recently,
Externí odkaz:
http://arxiv.org/abs/2401.05465
Autor:
Xu, Jianjin, Motamed, Saman, Vaddamanu, Praneetha, Wu, Chen Henry, Haene, Christian, Bazin, Jean-Charles, de la Torre, Fernando
Face inpainting is important in various applications, such as photo restoration, image editing, and virtual reality. Despite the significant advances in face generative models, ensuring that a person's unique facial identity is maintained during the
Externí odkaz:
http://arxiv.org/abs/2312.03556
Autor:
Zhang, Cheng, Chen, Xuanbai, Chai, Siqi, Wu, Chen Henry, Lagun, Dmitry, Beeler, Thabo, De la Torre, Fernando
Text-to-image generative models often reflect the biases of the training data, leading to unequal representations of underrepresented groups. This study investigates inclusive text-to-image generative models that generate images based on human-writte
Externí odkaz:
http://arxiv.org/abs/2309.05569
Autor:
Rai, Aashish, Gupta, Hiresh, Pandey, Ayush, Carrasco, Francisco Vicente, Takagi, Shingo Jason, Aubel, Amaury, Kim, Daeil, Prakash, Aayush, de la Torre, Fernando
In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle
Externí odkaz:
http://arxiv.org/abs/2304.12483