I will provide you with 15 images. The first 3 images are renders of the original scene, while the remaining images are renders of 3D insertions created by different models.

Your task is to evaluate the visual quality and realism of the inserted objects in these images. Please provide scores for three dimensions and include three additional supporting fields.

Evaluation Criteria:

Visual Quality Score (0-10):

Dimension: Assess the clarity, completeness, and overall appearance of the generated object.

Description: A score of 0 indicates severe distortion or an abnormal-looking object, while 10 signifies a perfect visual quality.

Content Rationality Score (0-10):

Dimension: Evaluate the object's realism and integrity, the logical interaction with the scene, its scale accuracy, and semantic consistency.

Description: A score of 0 means the content is distorted or contradictory, while 10 indicates that it perfectly aligns with real-world logic.

Geometric Accuracy Score (0-10):

Dimension: Check for proper integration with the original scene, ensuring there are no floating issues or mesh penetration, and that the contact is seamless.

Description: A score of 0 signifies severe floating or mesh penetration problems, while 10 represents a perfect fit.

Additional Fields:

Suggestions: Provide specific feedback for low-scoring dimensions (e.g., "suggest increasing object sharpness," "scene lighting is inconsistent").

Occlusion Status: Set hided_flag to True if a scene object is blocking the view, making the inserted object invisible. Otherwise, set it to False.

Important Considerations:

This evaluation should only focus on the visual quality and geometric fit of the inserted object. Do not evaluate the original scene for issues like a lack of realism or incorrect textures.

The models being tested can only insert new objects and cannot modify the original scene. Therefore, issues such as missing shadows or surface imprints on the original scene should not be factored into the scores.

Output Format:

You must return the result strictly in JSON format only. Do not include any other text (e.g., descriptive prefixes, explanations, etc.).

Example Output:

JSON

{
  "quality_score": 8.5,
  "rationality_score": 7.2,
  "geometry_score": 7.2,
  "hided_flag": true,
  "suggestions": ["Add more white space to the composition", "Background details are slightly blurry"],
  "prompt_infer": "Add a teddy bear on the bed"
}
The editing prompt for this evaluation is: <Prompt>