今回は「Self-conditioned Image Generation via Generating Representations」という論文について取り上げます。 なお以下に掲載する図表は全て論文からの引用です。 概要 この論文では「自己条件付き画像生成(RCG; Representation-Conditioned image Generation)」のフレームワークを提案しています。以下に自己条...
RCG: Self-conditioned Image Generation via Generating Representations TL; DR:将图像的无监督表征作为(自)条件(而非是将文本 prompt 作为条件),生成与原图语义内容一致的多样且高质量结果。视觉训练能不能 / 需不需要摆脱文本,仍有待研究。 引… 阅读全文 ...
The shape-conditioned image generation task is achieved by explicitly modeling the image appearance via a latent ap- pearance vector. The network is trained using unpaired training samples of real images and rendered normal maps. This approach enables us to generate images of arbitrary object ...
Convolutional Occupancy Networks Unconstrained Scene Generation with Locally Conditioned Radiance Fields Disentanglement (to-do:从两个方面探讨解耦 a. 特征空间的解耦;b.特征生成和神经渲染的解耦) Volume Rendering with Radiance Fields (to-do: 详述将implicit representation通过体渲染的方式如何渲染图像或者渲染特征...
The representation is learned by a self-supervised recurrent neural network, that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation. It is shown that the individuals' representation improves the next-frame ...
Another example is DALLE-2 [43], where latents conditioned on a pre-trained CLIP representation are used to create high- quality text-to-image generations. However, in computer vision, there are currently no widely adopted models that unify image generation and...
3. Diffusion autoencoders In the pursuit of a meaningful latent code, we design a conditional DDIM image decoder p(xt−1|xt, zsem) that is conditioned on an additional latent variable zsem, and a se- mantic encoder zsem = Encφ(x0) that learns to map an ...
quality, ranging from one to two orders of magnitude. The compactness of GaussianCube greatly eases the difficulty of 3D generative modeling. Extensive experiments conducted on unconditional and class-conditioned object generation, digital avatar creation, and text-to-3D synthesis all show that our ...
The generated vision- conditioned linguistic features are combined with the corre- sponding visual embedding to provide a granularity-specific representation for the referent object. Finally, we integrate the multi-level target-aware features and boundary infor-...
EVA is a vanilla ViT pre- trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of ...