5 papers
FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
Marco Pegoraro, Evan Atherton, Bruno Roy +2
Learning natural body motion remains challenging due to the strong coupling between spatial geometry and temporal dynamics. Embedding motion in phase manifolds, latent spaces that…
Unsupervised Representation Learning for 3D Mesh Parameterization with Semantic and Visibility Objectives
AmirHossein Zamani, Bruno Roy, Arianna Rampini
Recent 3D generative models produce high-quality textures for 3D mesh objects. However, they commonly rely on the heavy assumption that input 3D meshes are accompanied by manual me…
A Scalable Attention-Based Approach for Image-to-3D Texture Mapping
Arianna Rampini, Kanika Madan, Bruno Roy +2
High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference im…
Motion Generation: A Survey of Generative Approaches and Benchmarks
Aliasghar Khani, Arianna Rampini, Bruno Roy +5
Motion generation, the task of synthesizing realistic motion sequences from various conditioning inputs, has become a central problem in computer vision, computer graphics, and rob…
UniMoGen: Universal Motion Generation
Aliasghar Khani, Arianna Rampini, Evan Atherton +1
Motion generation is a cornerstone of computer graphics, animation, gaming, and robotics, enabling the creation of realistic and varied character movements. A significant limitatio…