25 citations · 41 across the 13 of their papers we have counts for
10 papers · 1 filter
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…
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…
Decoupling High and Low Frequencies for Faithful Image Generation with Fine Details
Tejaswini Medi, Hsien-Yi Wang, Arianna Rampini +1
Latent generative models compress images into learned embeddings prior to synthesis, and the generation quality critically depends on how faithfully these embeddings preserve visua…
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…
Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings
Aditya Sanghi, Aliasghar Khani, Pradyumna Reddy +5
Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute…
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
Tejaswini Medi, Arianna Rampini, Pradyumna Reddy +2
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike…
Make-A-Shape: a Ten-Million-scale 3D Shape Model
Ka-Hei Hui, Aditya Sanghi, Arianna Rampini +4
Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantia…
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation
Aditya Sanghi, Pradeep Kumar Jayaraman, Arianna Rampini +4
Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation. This motivates…
Instant recovery of shape from spectrum via latent space connections
Riccardo Marin, Arianna Rampini, Umberto Castellani +3
We introduce the first learning-based method for recovering shapes from Laplacian spectra. Given an auto-encoder, our model takes the form of a cycle-consistent module to map laten…