Search papers, labs, and topics across Lattice.
Forget grid layouts: Map2World lets you generate consistent 3D worlds from arbitrary segment maps, offering unprecedented control and scalability.
Simple, artist-friendly quad meshes can now be automatically generated on 3D shapes using a diffusion model trained on a continuous surface representation, sidestepping the complexity of discrete mesh optimization.
Edit 3D assets with text prompts while actually preserving the original object's unchanged parts, thanks to a new masking strategy and training dataset.
Real-time, lightweight image compression is now possible with diffusion models, thanks to a novel architecture that swaps transformers for convolutions and prioritizes compression-focused pre-training.
Iterative visual refinement lets agents navigate dense coding IDEs with superhuman precision, outperforming single-shot methods and paving the way for more reliable software engineering agents.
Even when source data is protected, source-free domain adaptation leaks knowledge of source-exclusive classes into the target domain, creating a privacy risk that can be mitigated with adversarial unlearning.
Autonomous driving models can now achieve remarkable zero-shot generalization by leveraging the power of large-scale video generation models to jointly predict future actions and visuals.
GeoAI assistants remain unproductive because they lack a crucial agency layer for iterative human-AI collaboration, a gap this paper addresses with nine core primitives.
Synthetic motion data, when represented as optical flow, unlocks a new level of realism and control in video diffusion models, surpassing the limitations of real-world datasets.
Ditch mean pooling in your geospatial foundation models: richer pooling methods like GeM can boost accuracy by up to 5% and slash the geographic generalization gap by 40%.
LLMs can now more accurately answer questions on complex documents thanks to a new system that understands layout and hierarchical relationships between document components.
Achieve up to 57% better point cloud compression by combining the generalization of pretrained models with the robustness of implicit neural representations.
ImageNet-pretrained CNNs can spot looted archaeological sites from space with surprising accuracy, leaving traditional methods in the dust.
