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Zhengqing Yuan, Zhaoxu Li, Lichao Sun
TinyGPT-V, built on Phi-2 with vision modules from BLIP-2 or CLIP, offers high performance with low computational requirements, enabling efficient multimodal large language modeling.
Wenyi Hong, Weihan Wang, Qingsong Lv +8 authors
CogAgent, a visual language model with strong GUI understanding and navigation capabilities, outperforms LLM-based methods in both PC and Android GUI tasks using only screenshots.
Shunsuke Saito, Gabriel Schwartz, Tomas Simon +2 authors
Relightable Gaussian Codec Avatars model high-fidelity head avatars with real-time relighting capabilities using 3D Gaussians for geometry and learnable radiance transfer for appearance.
Yuxuan Yan, Chi Zhang, Rui Wang +3 authors
A hybrid guidance framework for identity-preserving image synthesis efficiently generates stylistic portraits by combining stylized images, facial images, and textual prompts.
Jiasen Lu, Christopher Clark, Sangho Lee +5 authors
A unified autoregressive multimodal model handles image, text, audio, and action by tokenizing inputs into a shared space and processing them with a transformer architecture, achieving state-of-the-art performance across various benchmarks.
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu +5 authors
A study explores the effectiveness of alignment tuning in large language models, suggesting that superficial methods like in-context learning can achieve strong performance without traditional supervised fine-tuning or reinforcement learning.
Chenxu Zhang, Chao Wang, Jianfeng Zhang +7 authors
DREAM-Talk is a two-stage diffusion-based framework generating expressive emotional talking faces with accurate lip-sync from single portrait images.
Avi Singh, John D. Co-Reyes, Rishabh Agarwal +35 authors
Self-training with scalar feedback using expectation-maximization improves language model performance on tasks with verifiable answers, reducing reliance on human data.
Zengzhi Wang, Rui Xia, Pengfei Liu
A new large-scale, high-quality math-centric corpus named MathPile has been introduced to enhance mathematical reasoning abilities of language models.
Jianqiang Ren, Chao He, Lin Liu +10 authors
The Make-A-Character (Mach) framework uses large language and vision models to create realistic, 3D avatars from text descriptions efficiently, enabling customization and seamless integration with existing CG pipelines.
Miao Hua, Jiawei Liu, Fei Ding +3 authors
DreamTurner uses a novel approach by injecting reference information through subject encoders and self-subject-attention layers to enhance subject-driven image generation, balancing subject learning and model capabilities.
Saurabh Saxena, Junhwa Hur, Charles Herrmann +2 authors
A generic diffusion model with log-scale depth parameterization and FOV conditioning achieves state-of-the-art zero-shot metric depth estimation by handling indoor and outdoor scenes effectively and reducing relative error significantly.
Yifeng Ma, Shiwei Zhang, Jiayu Wang +3 authors
DreamTalk enables expressive talking head generation using diffusion models with specialized components for denoising, lip-sync, and style prediction.
Alexander Borzunov, Max Ryabinin, Artem Chumachenko +5 authors
A decentralized system pools idle compute resources to enable efficient inference and fine-tuning of large language models over a consumer-grade network, showcasing significant performance improvements.
Tianxing Wu, Chenyang Si, Yuming Jiang +2 authors
FreeInit addresses the temporal consistency and unnatural dynamics issues in diffusion-based video generation by refining spatial-temporal low-frequency components during inference, improving subject appearance and consistency.
Yuelang Xu, Benwang Chen, Zhe Li +4 authors
The method uses controllable 3D Gaussians and a fully learned MLP-based deformation field for high-fidelity 3D head avatar modeling under sparse views, employing geometry-guided initialization with implicit SDF and Deep Marching Tetrahedra for stability and high rendering quality.
Lingmin Ran, Xiaodong Cun, JiaWei Liu +5 authors
X-Adapter upgrades text-to-image diffusion models like SDXL to work with existing plugins without retraining, through feature remapping and a null-text training strategy.
En Yu, Liang Zhao, Yana Wei +8 authors
A novel MLLM, Merlin, is introduced which incorporates future modeling through FPT and FIT, enhancing foresight and future reasoning capabilities.
Bichen Wu, Ching-Yao Chuang, Xiaoyan Wang +6 authors
Fairy, an adaptation of image-editing diffusion models for video editing, uses anchor-based cross-frame attention to enhance temporal coherence and efficiency, outperforming existing methods.
Chaoning Zhang, Dongshen Han, Sheng Zheng +3 authors
A new approach improves MobileSAM's efficiency for the SegEvery task by directly generating valid masks, boosting performance and reducing computation time.
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