Search Results for author:
Found 7 papers, 4 papers with code
Text2VDM: Text to Vector Displacement Maps for Expressive and Interactive 3D Sculpting
This paper presents Text2VDM, a novel framework for text-to-VDM brush generation through the deformation of a dense planar mesh guided by score distillation sampling (SDS).
DEGAS: Detailed Expressions on Full-Body Gaussian Avatars
We also propose an audio-driven extension of our method with the help of 2D talking faces, opening new possibilities for interactive AI agents.
HeadEvolver: Text to Head Avatars via Expressive and Attribute-Preserving Mesh Deformation
Extensive experiments demonstrate that our framework can generate diverse and expressive head avatars with high-quality meshes that artists can easily manipulate in graphics software, facilitating downstream applications such as efficient asset creation and animation with preserved attributes.
MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising
Visual storytelling often uses nontypical aspect-ratio images like scroll paintings, comic strips, and panoramas to create an expressive and compelling narrative.
Efficient LLM Inference on CPUs
Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks.
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large.
Fast DistilBERT on CPUs
In this work, we propose a new pipeline for creating and running Fast Transformer models on CPUs, utilizing hardware-aware pruning, knowledge distillation, quantization, and our own Transformer inference runtime engine with optimized kernels for sparse and quantized operators.
