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The recently introduced Forward-Diffusion method allows to train a 3Ddiffusion model using only 2D images for supervision. However, it does noteasily generalise to different 3D representations and requires acomputationally expensive auto-regressive sampling process to generate theunderlying 3D scenes. In this paper, we propose GOEn: Gradient Origin Encoding(pronounced "gone"). GOEn can encode input images into any type of 3Drepresentation without the need to use a pre-trained image feature extractor.It can also handle single, multiple or no source view(s) alike, by design, andtries to maximise the information transfer from the views to the encodings. Ourproposed GOEnFusion model pairs GOEn encodings with a realisation of theForward-Diffusion model which addresses the limitations of the vanillaForward-Diffusion realisation. We evaluate how much information the GOEnmechanism transfers to the encoded representations, and how well it capturesthe prior distribution over the underlying 3D scenes, through the lens of apartial AutoEncoder. Lastly, the efficacy of the GOEnFusion model is evaluatedon the recently proposed OmniObject3D dataset while comparing to thestate-of-the-art Forward and non-Forward-Diffusion models and other 3Dgenerative models.

GOEnFusion: Gradient Origin Encodings for 3D Forward Diffusion Models