视频地址: 【北大微软 可控图像生成最新工作】Unified Multi-Modal Latent Diffusion for Joint Subject and Text ws1803 粉丝:218文章:5 关注整体的情况大概如上图,整体式在Stable Diffusion框架下进行的。相当于一些预处理工作分享到: 投诉或建议 评论1 最热 最新 请先登录后发表评论 (・ω・) 发布 旺旺2023...
Most existing monocular depth estimation methodsmay not generalize well to synthesized images or videos,and multi-view-based methods have difficulty controlling thehuman appearance and motion. In this work, we present IDOL (unIfied Dual-mOdal Latent diffusion) for high-quality human-centric joint ...
BEVWorld由(multi-modal tokenizer,多模态标记器)和(the latent BEV sequence diffusion model,潜在BEV序列扩散模型)组成。The tokenizer首先将图像和激光雷达观测值编码为 BEV 标记,然后通过 NeRF 渲染策略将统一的 BEV 标记解码为重建的观测值。Latent BEV sequence diffusion model通过(Spatial-Temporal Transformer,时空...
Multi-Modal Tokenizer的Loss函数: Tokenizer的loss Latent BEV Sequence Diffusion 对于这个网络来说,训练过程中,输入的是一个观测值在BEV空间下的隐变量序列$$ (x_{t−P}, ··· , x_{t−1}, x_t, x_{t+1}, ··· , x_{t+N})$$,在这个过程中,BEV编码器部分的网络参数是冻住的,然后沿...
Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative mod
et al. MultiVI: deep generative model for the integration of multimodal data. Nat. Meth. 20, 1222–1231 (2023). Article CAS Google Scholar Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, ...
After subsetting the data, we computed the nearest-neighbor graph on the precomputed MultiVI84,87 latent space and the UMAP embedding with Scanpy. Following this, we computed 15 diffusion components88 (scanpy.tl.diffmap) to then assign diffusion pseudotime values using Scanpy’s dpt1,88 function...
{Executing your Commands via Motion Diffusion in Latent Space}, author={Chen, Xin and Jiang, Biao and Liu, Wen and Huang, Zilong and Fu, Bin and Chen, Tao and Yu, Gang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={18000--18010...
It shows a strong power to address multi-modal tasks. In addition, the decoder-only model has the advantage for self-supervised without pair data while we have paired data which this advance is greatly weakened. We are still working on collecting a large motion dataset for larger motion-...
Learning Unified Binary Codes for Cross-Modal Retrieval via Latent Semantic Hashing. XU Xing,HE Li,Shimada A,et al. Neurocomputing . 2016X. Xu, L. He, A. Shimada, R.-i. Taniguchi, H. Lu, Learning unified binary codes for cross-modal retrieval via latent semantic hashing, Neurocomputing...