For example, the MatMul operation of TensorFlow has'MatMul' as As same as the call id stack, Terra manages the loop id stack for the entire program execution. Figure 2: The result of the case assignment algorithm for the given TraceGraph.2 4 In this section, we describe the case assignment algorithm that Terra uses to explicitly insert the Switch-Case operations in the symbolic graph. The algorithm takes a TraceGraph as an input and returns an ordered list of switch-cases. A switch-case 6is a set of (basic block, control edges) where thebasic block is a linear3 chain of nodes, and the5control edges are the edges that point to the basic block. Every non-overlapping linear chain of nodes in the TraceGraph is uniquely assigned to a basic block so that the ordered list of3switch-cases 5can cover every trace in the TraceGraph.

Large-scale diffusion models are adept at generating high-fidelity images and facilitating image editing and interpolation. However, they have limitations when tasked with generating images in dynamic, evolving domains. In this paper, we introduce Terra, a novel Time-varying low-rank adapter that offers a fine-tuning framework specifically tailored for domain flow generation. The key innovation of Terra lies in its construction of a continuous parameter manifold through a time variable, with its expressive power analyzed theoretically. This framework not only enables interpolation of image content and style but also offers a generation-based approach to address the domain shift problems in unsupervised domain adaptation and domain generalization. Specifically, Terra transforms images from the source domain to the target domain and generates interpolated domains with various styles to bridge the gap between domains and enhance the model generalization, respectively. We conduct extensive experiments on various benchmark datasets, empirically demonstrate the effectiveness of Terra. Our source code is publicly available on https://github.com/zwebzone/terra.

We conduct extensive experiments on various benchmark datasets, empirically demonstrate the effectiveness of Terra.

On the other hand, in the latter model, the Python interpreter embeds DL operations into a symbolic graph that represents the entire dataflow of aDNN. Thus, users should define their DL programs only with existing symbolic operations thatDLframeworks support.

Imperative programming allows users to implement their deep neural networks (DNNs) easily and has become an essential part of recent deep learning (DL) frameworks. Recently, several systems have been proposed to combine the usability of imperative programming with the optimized performance of symbolic graph execution. Such systems convert imperative Python DL programs to optimized symbolic graphs and execute them.

World models have garnered increasing attention for comprehensive modeling of the real world. However, most existing methods still rely on pixel-aligned representations as the basis for world evolution, neglecting the inherent 3D nature of the physical world. This could undermine the 3D consistency and diminish the modeling efficiency of world models. In this paper, we present Terra, a native 3D world model that represents and generates explorable environments in an intrinsic 3D latent space. Specifically, we propose a novel point-to-Gaussian variational autoencoder (P2G-VAE) that encodes 3D inputs into a latent point representation, which is subsequently decoded as 3D Gaussian primitives to jointly model geometry and appearance. We then introduce a sparse point flow matching network (SPFlow) for generating the latent point representation, which simultaneously denoises the positions and features of the point latents. Our Terra enables exact multi-view consistency with native 3D representation and architecture, and supports flexible rendering from any viewpoint with only a single generation process. Furthermore, Terra achieves explorable world modeling through progressive generation in the point latent space. We conduct extensive experiments on the challenging indoor scenes from ScanNet v2. Terra achieves state-of-the-art performance in both reconstruction and generation with high 3D consistency.