LLaVA-4D: Embedding SpatioTemporal Prompt into LMMs for 4D Scene Understanding
Hanyu Zhou, Gim Hee Lee
TL;DR
LLaVA-4D introduces a novel spatiotemporal prompt embedding for large multimodal models, enabling improved understanding of dynamic 4D scenes by encoding 3D positions and time, and aligning visual and language features.
Contribution
The paper proposes a new 4D spatiotemporal prompt embedding and a dataset for instruction fine-tuning LMMs, enhancing dynamic scene understanding capabilities.
Findings
Effective in distinguishing background from objects.
Improves understanding of dynamic scenes in 4D.
Demonstrates superior performance across tasks.
Abstract
Despite achieving significant progress in 2D image understanding, large multimodal models (LMMs) struggle in the physical world due to the lack of spatial representation. Typically, existing 3D LMMs mainly embed 3D positions as fixed spatial prompts within visual features to represent the scene. However, these methods are limited to understanding the static background and fail to capture temporally varying dynamic objects. In this paper, we propose LLaVA-4D, a general LMM framework with a novel spatiotemporal prompt for visual representation in 4D scene understanding. The spatiotemporal prompt is generated by encoding 3D position and 1D time into a dynamic-aware 4D coordinate embedding. Moreover, we demonstrate that spatial and temporal components disentangled from visual features are more effective in distinguishing the background from objects. This motivates embedding the 4D…
Peer Reviews
Decision·ICLR 2026 Poster
- The paper presents a new 4D spatial-temporal understanding framework for MLLMs. Some new designs for visual encoding and positional embedding are proposed. A new data recipe and a new benchmark for 4D understanding are constructed. - The new designs looks reasonable overall. Ablation studies show the effectiveness compared to baseline solutions.
- My main concern is the performance of the proposed method. A quite complex solution is proposed in the paper (with a new visual encoding solution and some new designs for embedding), but the results are not that impressive. Many recent methods, like Spatial MLLM [r1], 3UR-LLM [r2], and Coarse Correspondences [r3], that can achieve better performance on ScanQA are not compared or discussed. [r1] Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence [r2] 3UR-LLM: An En
- The dataset makes a significant contribution, and the method can significantly improve the ability to understand 4D scenes. - The writting is clear and motivation is easy to understand. - The three-stage training avoids the convergence difficulties caused by directly training 4D features, enabling the model to smoothly transition from basic 2D/3D capabilities to 4D capabilities.
- Data quality: 4D data relies on GPT-4V to extract spatiotemporal information and GPT to generate instructions, which may contain annotation errors (such as coordinate deviation and timestamp misalignment), affecting the model fine-tuning effect. However, the paper does not evaluate the impact of annotation errors on performance. - Temporal coding: it relies solely on optical flow to estimate motion information. However, optical flow is prone to inaccurate estimation in fast-moving or occluded
1. The LLaVA-4D model is a groundbreaking innovation, endowing large language models with 4D understanding, transcending previous 3D-focused limitations. 2. The authors have a clear reason for decoupling spatiotemporal visual features, and ablation experiments also prove that this decoupling module can make the representation of spatiotemporal features stronger. 3. The Chat4D dataset, developed by the authors, addresses a critical void in 4D scene understanding for multimodal large language mode
1. The paper lacks clarity on LLaVA-4D's capabilities, failing to specify its maximum video frame rate and duration. Additionally, it omits critical details about the 4D data in the Chat4D dataset, such as the average video length. 2. When comparing with state-of-the-art models, it seems that other models haven’t been trained or fine-tuned on Chat4D. (As shown in Table 1, they don’t even have the ability to output information related to the temporal dimension.) This makes it hard to directly sh
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning