FaceParts performs unsupervised segmentation of facial features in Gaussian Splatting avatars and supports precise editing and cross-avatar part transfer using feature disentanglement, density clustering, and FLAME anchoring.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Sparse Concept Anchoring biases neural latent spaces toward targeted concepts using under 0.1% labels per concept, enabling reversible steering via projection and permanent removal via weight ablation with minimal side effects on other features.
Nanomind decomposes LMMs into modular bricks mapped to heterogeneous accelerators with TABM zero-copy transfers, fused low-bit kernels, and a battery-aware scheduler, cutting energy 42.3% and enabling 18.8-hour runtime on a 2000 mAh battery for LLaVA-OneVision-Qwen2-0.5B.
citing papers explorer
-
FaceParts: Segmentation and Editing of Gaussian Splatting
FaceParts performs unsupervised segmentation of facial features in Gaussian Splatting avatars and supports precise editing and cross-avatar part transfer using feature disentanglement, density clustering, and FLAME anchoring.
-
Sparse Concept Anchoring for Interpretable and Controllable Neural Representations
Sparse Concept Anchoring biases neural latent spaces toward targeted concepts using under 0.1% labels per concept, enabling reversible steering via projection and permanent removal via weight ablation with minimal side effects on other features.
-
Tiny but Mighty: A Software-Hardware Co-Design Approach for Efficient Multimodal Inference on Battery-Powered Small Devices
Nanomind decomposes LMMs into modular bricks mapped to heterogeneous accelerators with TABM zero-copy transfers, fused low-bit kernels, and a battery-aware scheduler, cutting energy 42.3% and enabling 18.8-hour runtime on a 2000 mAh battery for LLaVA-OneVision-Qwen2-0.5B.