5 papers
Path-independent Flow Matching for Multi-parameter Generative Dynamics
Francisco Téllez, AmirHossein Zamani, Philippe Martin +5
Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formulation is not designed to capture multi-…
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…
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…
End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards
AmirHossein Zamani, Tianhao Xie, Amir G. Aghdam +2
While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challen…
Temporally Consistent Object Editing in Videos using Extended Attention
AmirHossein Zamani, Amir G. Aghdam, Tiberiu Popa +1
Image generation and editing have seen a great deal of advancements with the rise of large-scale diffusion models that allow user control of different modalities such as text, mask…