4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models

2503.10437

Wanhua Li et el.

🤗 2025-03-14

TL;DR
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Current methods struggle with dynamic 4D fields since CLIP can’t capture temporal dynamics in videos. Real-world environments are inherently dynamic, and vision models can’t achieve pixel-aligned, object-wise video features. This poses challenges for building an accurate 4D language field. Existing models extract global video-level features, overlooking specific object-level details, leading to difficulty in spatiotemporal semantic representation.

To address these challenges, 4D LangSplat uses multimodal large language models to generate detailed, temporally consistent captions for each object throughout a video. These captions serve as pixel-aligned, object-specific feature supervision, facilitating open-vocabulary queries. A status deformable network models smooth transitions exhibited by objects. This captures gradual transitions, enhancing the model’s temporal consistency.

Key Takeaways
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Why does it matter?
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4D LangSplat enables time-sensitive & open-ended language queries in dynamic scenes efficiently, bridging the gap between static scene understanding and real-world dynamics. This research provides a solid foundation for future work, helping understand complex scenes more easily.

Visual Insights
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🔼 This figure visualizes the language features learned by the 4D LangSplat model. The top two rows showcase the model’s ability to capture dynamic changes over time, such as the gradual diffusion of coffee. The bottom two rows demonstrate the model’s capacity to track changes in object state, specifically the opening and closing of a chicken container. Importantly, the figure highlights that 4D LangSplat maintains consistent semantic features for aspects of the scene that do not change over time, illustrating the precision of its semantic field through clear object boundaries.
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Figure 1: Visualization of the learned language features of our 4D LangSplat. We observe that 4D LangSplat effectively learns dynamic semantic features that change over time, such as the gradual diffusion of coffee shown in the first two rows, and the “chicken” toggling between open and closed states in the latter two rows. Additionally, our semantic field captures consistent features for semantics that remain unchanged over time, with the clear object boundaries in the visualization demonstrating the precision of our semantic field.

Method	americano		chickchicken		split-cookie		espresso		Average
Method	Acc(%)	vIoU(%)	Acc(%)	vIoU(%)	Acc(%)	vIoU(%)	Acc(%)	vIoU(%)	Acc(%)	vIoU(%)
LangSplat [38]	45.19	23.16	53.26	18.20	73.58	33.08	44.03	16.15	54.01	22.65
Deformable CLIP	60.57	39.96	52.17	42.77	89.62	75.28	44.85	20.86	61.80	44.72
Non-Status Field	83.65	59.59	94.56	86.28	91.50	78.46	78.60	47.95	87.58	68.57
Ours	89.42	66.07	96.73	90.62	95.28	83.14	81.89	49.20	90.83	72.26

🔼 This table presents a quantitative comparison of different methods’ performance on time-sensitive queries using the HyperNeRF dataset. It shows the accuracy (Acc) and Intersection over Union (IoU) scores for four different query types (americano, chickchicken, split-cookie, espresso). The results are presented for four methods: LangSplat, Deformable CLIP, Non-Status Field, and the proposed method (Ours). The table allows for a direct comparison of the proposed method’s performance against existing state-of-the-art techniques in handling time-sensitive queries for dynamic scenes.
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Table 1: Quantitative comparisons of time-sensitive querying on the HyperNeRF [37] dataset.

In-depth insights
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4D LangSplat
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The term ‘4D LangSplat’ evokes a novel approach to scene representation, extending the concept of 3D Gaussian Splatting (3D-GS) into the temporal dimension. It suggests incorporating language understanding into a 4D scene representation, enabling users to query dynamic scenes with natural language. The ‘LangSplat’ part implies a fusion of language embeddings with the Gaussian primitives, allowing for semantic queries. Challenges include capturing temporal coherence, handling dynamic object states, and effectively fusing visual and textual information. Success hinges on innovative techniques for learning semantic representations in dynamic scenes and efficiently rendering them in real-time. It likely involves leveraging large language models (LLMs) and multimodal learning to bridge the gap between visual and textual domains for 4D scene understanding.

Status Network
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The paper introduces a ‘status deformable network’ to explicitly model the continuous changes in dynamic scenes. This network constrains the Gaussian point’s semantic features to evolve within a predefined set of states. This approach enriches reconstruction quality and enhance temporal consistency, which means the model will capture the gradual transitions across object states, it also prevents any abrupt shifts in its semantic features. The status deformable network design demonstrates its adaptability to both spatial and temporal context, by capturing the nuances of evolving object states, therefore providing better open-world querying.

MLLM Prompting
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In the context of this research paper, MLLM prompting emerges as a crucial technique for enhancing the understanding and manipulation of dynamic 4D scenes. MLLMs, or Multimodal Large Language Models, are utilized to generate detailed, temporally consistent captions for objects throughout a video, overcoming limitations of vision-based feature supervision. The core idea is to leverage the capacity of MLLMs to process both visual and textual inputs, enabling the creation of rich, object-specific descriptions that capture the evolving semantics of the scene. This approach contrasts traditional methods that rely on static image-text matching, which struggle to capture the temporal dynamics inherent in video data. Prompt engineering becomes essential, employing both visual and textual cues to guide the MLLM towards generating captions focused on specific objects and their actions within the video. By converting video data into object-level captions, the research facilitates the training of a 4D language field that can effectively handle both time-agnostic and time-sensitive open-vocabulary queries, marking a significant advancement in the field.

Dynamic Semantics
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The research addresses dynamic semantics in 4D language fields, recognizing the limitations of CLIP in capturing temporal changes. Real-world scenes evolve, requiring models to understand object transformations and time-sensitive queries. The challenge lies in obtaining pixel-aligned, object-level features from videos, as current models often provide global features. The solution is 4D LangSplat, which learns directly from text generated by MLLMs rather than relying on visual features. This involves multimodal object-wise video prompting to generate detailed captions and a status deformable network to model smooth transitions between object states, ultimately supporting accurate and efficient time-sensitive queries.

4D Querying
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4D Querying leverages both time-agnostic and time-sensitive semantic fields. The time-agnostic aspect handles unchanging object attributes, utilizing relevance scores for segmentation masks. Time-sensitive queries combine both semantic fields; the former creates an initial mask, while the latter refines it to specific timeframes based on cosine similarity. A threshold determines relevant time segments, and the time-agnostic mask is retained as the final mask prediction. This enables capturing both persistent and dynamic object characteristics in scenes.

More visual insights
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More on tables

Method	HyperNeRF		Neu3D
Method	mIoU	mAcc	mIoU	mAcc
Feature-3DGS [67]	36.63	74.02	34.96	87.12
Gaussian Grouping [63]	50.49	80.92	49.93	95.05
LangSplat [38]	74.92	97.72	61.49	91.89
Ours	82.48	98.01	85.11	98.32

🔼 This table presents a quantitative comparison of the performance of different methods on time-agnostic querying tasks. Two datasets are used for evaluation: HyperNeRF [37] and Neu3D [27]. The metrics used for comparison are mean Intersection over Union (mIoU) and mean Accuracy (mAcc). Higher values for both mIoU and mAcc indicate better performance in accurately identifying and segmenting objects within the scene in response to open-vocabulary queries.
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Table 2: Quantitative comparisons of time-agnostic querying on the HyperNeRF [37] and Neu3D [27] datasets (Numbers in %).

Blur	Gray	Contour	$\Delta_{\mathrm{sim}}$
$\checkmark$			0.33
$\checkmark$	$\checkmark$		2.15
$\checkmark$	$\checkmark$	$\checkmark$	3.32

🔼 This table presents the ablation study results on the impact of varying the number of semantic states (K) in the status deformable network for the ‘chick_chicken’ query on the HyperNeRF dataset. It shows the accuracy (Acc) and Intersection over Union (IoU) metrics obtained when using different values for K, illustrating how the choice of K affects the model’s performance in capturing the nuanced transitions between semantic states of the ‘chick_chicken’ object (e.g., closed versus open).
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Table 5: Results for different state numbers on chick_chicken.

Video	Image	$\Delta_{\mathrm{sim}}$
		0.14
$\checkmark$		1.01
$\checkmark$	$\checkmark$	3.32

🔼 This table details the specific text prompts used in the multimodal object-wise video prompting method. The prompts guide the large language model (LLM) in generating captions for objects within video frames. It explains how visual prompts (red contour, grayscale, blur) are used to focus the LLM’s attention on the object of interest while providing background context for temporal consistency. The prompts are designed to ensure that the generated captions accurately describe the object’s state in the specific frame, avoiding extraneous information or redundant descriptions.
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Table 6: Details of Text prompts

K	2	3	4	5	6
Acc (%)	94.56	97.82	95.65	94.56	94.56
vIoU (%)	88.05	91.93	89.11	88.98	86.28

🔼 This table compares the performance of different methods in terms of frames per second (FPS) for processing queries. It shows the speed at which each method can handle queries, highlighting the efficiency gains of the proposed 4D LangSplat approach compared to baselines like Gaussian Grouping. This is crucial for real-time applications that require fast response times.
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Table 7: Query Performance Comparison.

Video prompts

Image prompts

I highlighted the objects I want you to describe in red outline and blurred the objects that don’t need you to describe. First please determine the object highlighted in red line in the video. Then briefly summarize the transformation process of this object.

You have an understanding of the overall transformation process of the object: {video prompt}. Now, I have provided you with images extracted from this process. Please describe the specific state of the object(s) in the given image, without referring to the entire video process. Avoid describing states that you can’t infer directly from the picture. Avoid repeating descriptions in context. For example, if the context suggests the object is moving up and down but the image shows it is just moving down, explicitly only state that the object is in a moving down state. If the context suggests the object is breaking but the image shows it is complete right now, explicitly only state that the object appears to be complete. If context tells you something changes from green to blue, but it’s blue in this image, just state that the object is blue.

🔼 Table 8 presents a quantitative comparison of the performance of different methods on the HyperNeRF dataset for time-agnostic semantic segmentation. It shows the mean Intersection over Union (mIoU) and mean Accuracy (mAcc) achieved by several approaches including Feature-3DGS, Gaussian Grouping, LangSplat, and the proposed method (Ours). The comparison is done across multiple object categories (americano, chickchicken, split-cookie, espresso, keyboard, torchocolate). This table allows for a direct assessment of the proposed approach’s effectiveness compared to state-of-the-art methods in achieving precise and accurate semantic segmentation in dynamic scenes.
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Table 8: Comparison of mean IoU and mean Accuracy for various methods on the HyperNeRF [37] datasets.

Method	FPS
Gaussian Grouping [63]	1.47
Ours-agnostic	5.24
Ours-sensitive	4.05

🔼 This table presents a quantitative comparison of the performance of different methods on the Neu3D dataset [27] for time-agnostic open-vocabulary queries. The metrics used are mean Intersection over Union (mIoU) and mean Accuracy (mAcc). Multiple semantic categories are evaluated, and the results show the performance of Feature-3DGS [67], Gaussian Grouping [63], LangSplat [38], and the proposed method (Ours). This allows for a direct comparison of the proposed approach against existing state-of-the-art methods in terms of both accuracy and efficiency in handling dynamic scenes.
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Table 9: Comparison of mean IoU and mean Accuracy for various methods on the Neu3D [27] dataset.

Method	americano		chickchicken		split-cookie
Method	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)
Feature-3DGS [67]	34.65	62.96	47.21	87.22	47.03	68.25
Gaussian Grouping [63]	61.77	71.31	34.65	75.52	72.71	96.56
LangSplat [38]	72.08	97.61	75.98	97.86	76.54	97.32
Ours	83.48	98.77	86.50	98.81	90.04	98.67
Method	espresso		keyboard		torchocolate
Method	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)
Feature-3DGS [67]	24.04	80.13	42.14	80.98	24.71	64.58
Gaussian Grouping [63]	32.45	82.46	42.44	74.15	58.95	85.52
LangSplat [38]	82.93	98.66	72.42	96.75	69.55	98.09
Ours	83.52	97.95	79.53	95.71	71.79	98.10

🔼 This table presents the accuracy results of video classification using MLLM-based embeddings. It compares the performance of the proposed method against state-of-the-art methods (IMP) on three widely used video classification datasets: HMDB51, UCF101, and Kinetics400. The results demonstrate the effectiveness of MLLM-based embeddings in video classification, even without fine-tuning, achieving competitive accuracy scores compared to specialized methods. The table highlights the ability of the MLLM-generated embeddings to capture spatiotemporal information relevant for video understanding tasks.
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Table 10: Accuracy Results (%) on the Video Classification task.

Method	coffee martini		cook spinach		cut roasted beef
Method	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)
Feature-3DGS [67]	30.23	84.74	41.50	95.59	31.66	91.07
Gaussian Grouping [63]	71.37	97.34	46.45	93.79	54.70	93.25
LangSplat [38]	67.97	98.47	78.29	98.60	36.53	97.04
Ours	85.16	99.23	85.09	99.38	85.32	99.28
Method	flame salmon		flame steak		sear steak
Method	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)	mIoU(%)	mAcc(%)
Feature-3DGS [67]	54.33	77.13	27.27	88.23	24.78	85.94
Gaussian Grouping [63]	35.72	94.69	36.92	95.96	54.44	95.27
LangSplat [38]	66.01	82.16	64.05	97.77	78.29	98.60
Ours	89.88	94.35	88.44	98.27	76.78	99.38

🔼 Table 11 presents the results of a spatial-temporal action localization experiment conducted on the UCF101 dataset [44]. The table compares the performance of a method using Multimodal Large Language Model (MLLM)-based embeddings against a state-of-the-art (SOTA) method, HIT [12]. The performance is evaluated using mean average precision (mAP) at three different Intersection over Union (IoU) thresholds (0.1, 0.2, and 0.5). This demonstrates the ability of MLLM-based embeddings to capture spatio-temporal information, even in a zero-shot setting.
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Table 11: Spatial-Temporal Action Localization Results (%) on UCF101 [44].

TL;DR#

Key Takeaways#

Why does it matter?#

Visual Insights#

In-depth insights#

4D LangSplat#

Status Network#

MLLM Prompting#

Dynamic Semantics#

4D Querying#

More visual insights#

Full paper#