Search papers, labs, and topics across Lattice.
Automated view scheduling in SceneFrom3D transforms the landscape of outdoor 3D scene generation, enabling unprecedented control over object appearance and geometry.
Stochastic attention isn't just a regularizer; it fundamentally transforms how visual information is learned in VLMs, leading to more stable and reliable models.
LUNA achieves realistic 3D human animation from 2D inputs without the limitations of traditional skinning methods, enabling unprecedented flexibility and expressivity.
FiCA generates photorealistic avatars from a single image, achieving unprecedented visual quality and identity fidelity without the need for individual optimization.
Relying on causal relationships rather than strong inductive biases, TDV achieves state-of-the-art performance in visual representation learning, challenging the status quo of self-supervised methods.
Forget task-specific fine-tuning – teaching VLMs basic geometry yields a +29% boost on spatial reasoning benchmarks.
Backpropagation's gradients, while predictive of high-level visual cortex activity, march to a different hierarchical beat than the brain itself, challenging its status as a biologically plausible learning mechanism.
Classical SfM can get stuck, and feedforward reconstruction can be brittle, but combining them creates a system that's both robust and accurate.
Skip the costly data collection for new eye-tracking devices: GazePrior synthesizes realistic training data by learning a 3D prior of human eyes, enabling zero-shot transfer.
Camera pose, largely ignored in video LLMs, unlocks significant gains in spatial reasoning and even improves general video QA when used as a lightweight supervisory signal.
Controllable 3D generation takes a leap forward with 3D-ReGen, a framework that leverages an initial 3D shape for tasks like enhancement and editing, outperforming existing methods.
Human motion generation gets a dose of reality: IAM shows that explicitly modeling body morphology and identity leads to more realistic and consistent movements.
Current egocentric video benchmarks miss the mark: EgoEverything uses human gaze to create questions that actually reflect how people behave, not just what they see.
A million videos with paired depth, camera pose, and 3D point tracks could unlock a new wave of 3D-aware video models.
Scaling robot learning with human data isn't a simple "more is better" equation; alignment with robot learning objectives is key.
Training 3D avatar diffusion models on millions of in-the-wild videos is now possible, thanks to a clever 3D tokenization and visibility-aware training strategy that overcomes partial observability.
Music-grounded video editing can now produce significantly more coherent timelines thanks to a novel global-local coordination mechanism that resolves cross-segment conflicts.
Stop avatars from looking like they're having a seizure: this method uses autoregressive prediction of appearance latents to create temporally stable and high-fidelity 3D Gaussian avatars.
Unlocking the secrets of viral video ads: a new MLLM framework reveals which initial moments hook viewers and drive conversions.
By surgically intervening in MLLM decoding, this work cuts hallucination rates without sacrificing descriptive quality, a feat prior methods struggled to achieve.
