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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.15030 |
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| _version_ | 1866914333186850816 |
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| author | Yue, Kaiyu Jia, Menglin Hou, Ji Goldstein, Tom |
| author_facet | Yue, Kaiyu Jia, Menglin Hou, Ji Goldstein, Tom |
| contents | We introduce the Sphere Encoder, an efficient generative framework capable of producing images in a single forward pass and competing with many-step diffusion models using fewer than five steps. Our approach works by learning an encoder that maps natural images uniformly onto a spherical latent space, and a decoder that maps random latent vectors back to the image space. Trained solely through image reconstruction losses, the model generates an image by simply decoding a random point on the sphere. Our architecture naturally supports conditional generation, and looping the encoder/decoder a few times can further enhance image quality. Across several datasets, the sphere encoder approach yields performance competitive with state of the art diffusions, but with a small fraction of the inference cost. Project page is available at https://sphere-encoder.github.io . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15030 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Image Generation with a Sphere Encoder Yue, Kaiyu Jia, Menglin Hou, Ji Goldstein, Tom Computer Vision and Pattern Recognition We introduce the Sphere Encoder, an efficient generative framework capable of producing images in a single forward pass and competing with many-step diffusion models using fewer than five steps. Our approach works by learning an encoder that maps natural images uniformly onto a spherical latent space, and a decoder that maps random latent vectors back to the image space. Trained solely through image reconstruction losses, the model generates an image by simply decoding a random point on the sphere. Our architecture naturally supports conditional generation, and looping the encoder/decoder a few times can further enhance image quality. Across several datasets, the sphere encoder approach yields performance competitive with state of the art diffusions, but with a small fraction of the inference cost. Project page is available at https://sphere-encoder.github.io . |
| title | Image Generation with a Sphere Encoder |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.15030 |