Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data.

A batch size of 2048 is used during training with a learning rate of 1e-4. Both training and rendering were conducted using A WS. A.2 PixelNeRF We used a constant learning rate of 1e-4. To train PixelNeRF on Objaverse-XL we render the meshes in Blender. Each model is normalize to a bounding cube. We believe that models such as Zero123-XL, and those trained on Objaverse-XL, will enhance the ease of 3D content creation, enabling broader accessibility for individuals and businesses to participate.

In practice,θ is typically chosen as a neural network, for which it is often impossible to evaluate the induced densityµθ.

We thank the reviewers for their useful feedback and their time. We have corrected all the minor comments, as suggested. We now provide specific answers to each reviewer below. We thank the reviewer for their positive evaluation of our work and their comments. "All the theoretical contributions seem to me a bit marginal" Since the Sliced-Wasserstein distance is an average of one-6 We will explain these observations more explicitly to clarify our contributions.

Neural Information Processing Systems http://nips.cc/