From these temporally-aware slots, the training objective is to reconstruct the middle frame in a high-level semantic feature space. We propose a masking strategy by dropping a significant portion of tokens in the feature space for efficiency and regularization. Additionally, we address over-clustering by merging slots based on similarity.

In this supplementary document, we first provide details on the network architectures and training strategy of our approach in Section 1. Our settings for the baselines are described in Section 2. Section 3 shows additional results and failure cases. In Section 4, we discuss the societal impact of our work.

In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction.

Next, we provide details on the NA VSIM implementation and the dataset used in our study.

Section 3 presents uncurated samples for both baselines and our approach. Section 4 reports additional experiments. Lastly, we provide details on training configurations, hyperparameters, and compute in Section 5. Code, models, and supplementary videos can be found on the project page https://sites. The following proof follows the consistency proofs in [23] and [7]. We now show that the result above still holds when applying stochastic differentiable augmentations before the feature projections.

Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train.

We argue that this formulation is more natural, as it makes A TISS generally useful beyond fully automatic room layout synthesis. For example, the same trained model can be used in interactive applications for general scene completion, partial room rearrangement with any objects specified by the user, as well as object suggestions for any partial room.

Next, we provide details on the NA VSIM implementation and the dataset used in our study.