Temporal-Visual Semantic Alignment: A Unified Architecture for Transferring Spatial Priors from Vision Models to Zero-Shot Temporal Tasks

2025-11-24

AI Summary

This paper addresses the semantic alignment challenge in non-visual time-series-to-high-fidelity-image generation. We propose TimeArtist, a novel framework that introduces two key innovations: (1) a “warm-up–alignment” two-stage training paradigm—first performing self-supervised pretraining via dual autoencoders with a shared vector quantizer, then freezing encoders and introducing a representation-layer projection to achieve time-vision cross-modal semantic alignment; and (2) a transferable spatial prior unified architecture that maps temporal dynamics into controllable visual styles. Experiments demonstrate state-of-the-art performance on image quality metrics (e.g., FID, LPIPS) and achieve SOTA results on downstream zero-shot time-series forecasting tasks. These findings validate the effectiveness of both cross-modal semantic alignment and generative representation transfer.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Large Multimodal Models (LMMs) have achieved remarkable progress in aligning and generating content across text and image modalities. However, the potential of using non-visual, continuous sequential, as a conditioning signal for high-fidelity image generation remains largely unexplored. Furthermore, existing methods that convert series into "pseudo-images" for temporal forecasting fail to establish semantic-level alignment. In this paper, we propose TimeArtist, a temporal-visual conversion framework that pioneers semantic-level alignment between time series fluctuations and visual concepts. It pioneers a "warmup-align" paradigm: first, a dual-autoencoder and shared quantizer are self-supervised trained on large-scale datasets to learn modality-shared representations. Then, the encoders and quantizer are frozen, and a projection is introduced to align temporal and visual samples at the representation level. TimeArtist establishes a versatile cross-modal framework, enabling high-quality, diverse image generation directly from time series, while capturing temporal fluctuation patterns to render images as styles transfer. Extensive experiments show that TimeArtist achieves satisfactory performance in image generation metrics, while also attaining superior results in zero-shot temporal tasks. Our work establishes a new paradigm for cross-modal generation, bridging the gap between temporal dynamics and visual semantics.

Problem

• Transferring spatial priors from vision models to temporal tasks
• Establishing semantic alignment between time series and visual concepts
• Enabling high-fidelity image generation directly from temporal data

Innovation

• Semantic alignment between time series and visual concepts
• Dual-autoencoder with shared quantizer for cross-modal learning
• Frozen encoders with projection enable zero-shot temporal tasks