diff --git a/pytorch3d/implicitron/dataset/types.py b/pytorch3d/implicitron/dataset/types.py
index d1dd0a48..497b91ee 100644
--- a/pytorch3d/implicitron/dataset/types.py
+++ b/pytorch3d/implicitron/dataset/types.py
@@ -9,7 +9,7 @@ import dataclasses
 import gzip
 import json
 from dataclasses import dataclass, Field, MISSING
-from typing import Any, cast, IO, Optional, Tuple, Type, TypeVar, Union
+from typing import Any, cast, Dict, IO, Optional, Tuple, Type, TypeVar, Union
 
 import numpy as np
 from pytorch3d.common.datatypes import get_args, get_origin
@@ -80,6 +80,7 @@ class FrameAnnotation:
     depth: Optional[DepthAnnotation] = None
     mask: Optional[MaskAnnotation] = None
     viewpoint: Optional[ViewpointAnnotation] = None
+    meta: Optional[Dict[str, Any]] = None
 
 
 @dataclass
@@ -169,9 +170,11 @@ def _dataclass_list_from_dict_list(dlist, typeannot):
 
     cls = get_origin(typeannot) or typeannot
 
+    if typeannot is Any:
+        return dlist
     if all(obj is None for obj in dlist):  # 1st recursion base: all None nodes
         return dlist
-    elif any(obj is None for obj in dlist):
+    if any(obj is None for obj in dlist):
         # filter out Nones and recurse on the resulting list
         idx_notnone = [(i, obj) for i, obj in enumerate(dlist) if obj is not None]
         idx, notnone = zip(*idx_notnone)
@@ -180,8 +183,13 @@ def _dataclass_list_from_dict_list(dlist, typeannot):
         for i, obj in zip(idx, converted):
             res[i] = obj
         return res
+
+    is_optional, contained_type = _resolve_optional(typeannot)
+    if is_optional:
+        return _dataclass_list_from_dict_list(dlist, contained_type)
+
     # otherwise, we dispatch by the type of the provided annotation to convert to
-    elif issubclass(cls, tuple) and hasattr(cls, "_fields"):  # namedtuple
+    if issubclass(cls, tuple) and hasattr(cls, "_fields"):  # namedtuple
         # For namedtuple, call the function recursively on the lists of corresponding keys
         types = cls._field_types.values()
         dlist_T = zip(*dlist)
@@ -240,10 +248,15 @@ def _dataclass_list_from_dict_list(dlist, typeannot):
 
 
 def _dataclass_from_dict(d, typeannot):
-    cls = get_origin(typeannot) or typeannot
-    if d is None:
+    if d is None or typeannot is Any:
         return d
-    elif issubclass(cls, tuple) and hasattr(cls, "_fields"):  # namedtuple
+    is_optional, contained_type = _resolve_optional(typeannot)
+    if is_optional:
+        # an Optional not set to None, just use the contents of the Optional.
+        return _dataclass_from_dict(d, contained_type)
+
+    cls = get_origin(typeannot) or typeannot
+    if issubclass(cls, tuple) and hasattr(cls, "_fields"):  # namedtuple
         types = cls._field_types.values()
         return cls(*[_dataclass_from_dict(v, tp) for v, tp in zip(d, types)])
     elif issubclass(cls, (list, tuple)):
@@ -315,3 +328,15 @@ def load_dataclass_jgzip(outfile, cls):
     """
     with gzip.GzipFile(outfile, "rb") as f:
         return load_dataclass(cast(IO, f), cls, binary=True)
+
+
+def _resolve_optional(type_: Any) -> Tuple[bool, Any]:
+    """Check whether `type_` is equivalent to `typing.Optional[T]` for some T."""
+    if get_origin(type_) is Union:
+        args = get_args(type_)
+        if len(args) == 2 and args[1] == type(None):  # noqa E721
+            return True, args[0]
+    if type_ is Any:
+        return True, Any
+
+    return False, type_
diff --git a/tests/implicitron/test_types.py b/tests/implicitron/test_types.py
index 91338edc..56352b93 100644
--- a/tests/implicitron/test_types.py
+++ b/tests/implicitron/test_types.py
@@ -85,6 +85,10 @@ class TestDatasetTypes(unittest.TestCase):
         self._compare_with_scalar([dct], List[FrameAnnotation])
         self._compare_with_scalar({"k": dct}, Dict[str, FrameAnnotation])
 
+        dct2 = dct.copy()
+        dct2["meta"] = {"d": 76}
+        self._compare_with_scalar(dct2, FrameAnnotation)
+
     def _compare_with_scalar(self, obj, typeannot, repeat=3):
         input = [obj] * 3
         vect_output = types._dataclass_list_from_dict_list(input, typeannot)